Methods and apparatus for estimating point-of-gaze in three dimensions

ABSTRACT

Methods for determining a point-of-gaze (POG) of a user in three dimensions are disclosed. In particular embodiments, the methods involve: presenting a three-dimensional scene to both eyes of the user; capturing image data including both eyes of the user; estimating first and second line-of-sight (LOS) vectors in a three-dimensional coordinate system for the user&#39;s first and second eyes based on the image data; and determining the POG in the three-dimensional coordinate system using the first and second LOS vectors.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.12/600,238 which is a PCT national phase entry (371) applicationcorresponding to PCT/CA2008/000987 having an international filing dateof 23 May 2008. PCT/CA2008/000987 claims priority from U.S. applicationNo. 60/939,840 filed 23 May 2007 and U.S. application No. 61/071,372filed 24 Apr. 2008. The patents and patent applications referred to inthis paragraph are all hereby incorporated herein by reference.

TECHNICAL FIELD

The invention relates to sensing and tracking eye-gaze characteristicsand to methods and apparatus for using this information to estimate apoint-of-gaze in three dimensions.

BACKGROUND

Common techniques for interaction between humans and machines includehand-operated user interface devices, such as keyboards, buttons,joysticks and pointing devices (e.g. a mouse). Recent developments ineye-gaze tracking systems can determine the line-of-sight (LOS) vectorof an individual's eye. This LOS information can be used as a controltool for human machine interaction. There are a number of advantages ofusing eye-gaze tracking information as a control tool. These advantagesinclude: the intuitive link between the visual system of the eye and theresultant images in the brain; the speed of eye movement relative tomoving a hand-operated interaction device (i.e. users typically look atthe desired destination of a hand-operated device prior to moving thehand-operated device); and the possibility that eye-gaze trackingtechniques may be used by severely disabled individuals.

A number of other applications for eye-gaze tracking systems include,without limitation: psychological and physiological research into theconnection between eye movements and perceptual and/or cognitiveprocesses; the analysis of driver awareness; research into theeffectiveness of advertising and website layouts; and gaze contingentdisplays.

A number of prior art references describe various techniques foreye-gaze tracking. These references include:

-   A. T. Duchowski, Eye Tracking Methodology: Theory and Practice.    Springer-Verlag, 2003.-   L. Young and D. Sheena, “Methods & designs: survey of eye movement    recording methods,” Behav. Res. Methods Instrum., vol. 5, pp.    397-429, 1975.-   R. Jacob and K. Karn, The Mind's Eye: Cognitive and Applied Aspects    of Eye Movement Research. Amsterdam: Elsevier Science, 2003, ch. Eye    Tracking in Human-Computer Interaction and Usability Research: Ready    to Deliver the Promises (Section Commentary), pp. 573-605.-   T. Hutchinson, J. White, W. Martin, K. Reichert, and L. Frey,    “Human-computer interaction using eye-gaze input,” Systems, Man and    Cybernetics, IEEE Transactions on, vol. 19, no. 6, pp. 1527-1534,    November-December 1989.-   S.-W. Shih and J. Liu, “A novel approach to 3-d gaze tracking using    stereo cameras,” Systems, Man and Cybernetics, Part B, IEEE    Transactions on, vol. 34, no. 1, pp. 234-245, February 2004.-   D. Beymer and M. Flickner, “Eye gaze tracking using an active stereo    head,” in IEEE Computer Society Conference on Computer Vision and    Pattern Recognition, vol. 2, 18-20 Jun. 2003, pp. II-451-8 vol. 2.-   C. Hennessey, B. Noureddin, and P. Lawrence, “A single camera    eye-gaze tracking system with free head motion,” in Proceedings of    the 2006 symposium on Eye tracking research & applications. New    York, N.Y., USA: ACM Press, 2006, pp. 87-94.-   C. H. Morimoto, A. Amir, M. Flickner, “Detecting Eye Position and    Gaze from a Single Camera and 2 Light Sources,” 16th International    Conference on Pattern Recognition (ICPR'02)—Volume 4, 2002, p.    40314.-   Z. Zhu and Q. Ji, “Eye Gaze Tracking Under Natural Head Movements,”    Proceedings of the 2005 IEEE Computer Society Conference on Computer    Vision and Pattern Recognition, 2005.-   E. Guestrin and M. Eizenman, “General theory of remote gaze    estimation using the pupil center and corneal reflections,”    Biomedical Engineering, IEEE Transactions on, vol. 53, no. 6, pp.    1124-1133, June 2006.-   A. T. Duchowski, V. Shivashankaraiah, T. Rawls, A. K.    Gramopadhye, B. J. Melloy, and B. Kanki, “Binocular eye tracking in    virtual reality for inspection training,” in Proceedings of the 2000    symposium on Eye tracking research & applications. New York, N.Y.,    USA: ACM Press, 2000, pp. 89-96.-   K. Essig, M. Pomplun, and H. Ritter, “Application of a novel neural    approach to 3d gaze tracking: Vergence eye-movements in    autostereograms,” in Proceedings of the 26thl Meeting of the    Cognitive Science Society, K. Forbus, D. Gentner, and T. Regier,    Eds., 2004, pp. 357-362.-   K. Essig, M. Pomplun, and H. Ritter, “A neural network for 3d gaze    recording with binocular eyetrackers,” International Journal of    Parallel, Emergent and Distributed Systems (accepted), 2006.-   Y.-M. Kwon and K.-W. Jeon, “Gaze computer interaction on stereo    display,” in Proceedings of the 2006 ACM SIGCHI international    conference on Advances in computer entertainment technology. New    York, N.Y., USA: ACM Press, 2006, p. 99.-   PCT publication No. WO04/045399 (Elvesjö et al.).-   U.S. Pat. No. 4,386,670 (Hutchinson).-   U.S. Pat. No. 5,231,674 (Cleveland et al.).-   U.S. Pat. No. 5,471,542 (Ragland).-   U.S. Pat. No. 5,428,413 (Shindo).-   U.S. Pat. No. 6,152,563 (Hutchinson et al.).-   U.S. Pat. No. 6,659,611 (Amir et al.).-   U.S. Pat. No. 5,481,622 (Gerhardt).-   U.S. Pat. No. 6,578,962 (Amir et al.).

Some of these prior art eye-gaze tracking systems may be used to detectLOS information for one of a user's eyes when the user's eye is fixatedat a particular location (referred to as a point-of-gaze (POG)). An eyemay be said to be “fixated” on a POG when the POG is imaged onto theeye's fovea and the motion of the eye is stabilized. To the extent thatprior art eye-gaze tracking systems are used to estimate a POG using LOSinformation, the LOS is only used to estimate the POG in two dimensions.For example, where a user's eye is fixated on a two-dimensional monitorscreen, the POG may be determined to be the location where the LOSvector intersects with the plane of the monitor screen.

Two-dimensional POG estimation may be satisfactory for interacting withstandard two-dimensional human-machine interface environments (e.g.monitor screens). However, there are a number of continually improvingthree dimensional display technologies, such as volumetric displays andparallax beam splitter displays, for example, which may providethree-dimensional human-machine interface environments—see, for example,M. Halle, “Autostereoscopic displays and computer graphics,” SIGGRAPHComput. Graph., vol. 31, no. 2, pp. 58-62, 1997. Such three-dimensionaluser interface environments could provide users with a much richerexperience (i.e. more functionality) than existing two-dimensional userinterface environments.

For this and other reasons, there is a general desire to provide methodsand apparatus for POG estimation in three dimensions.

BRIEF DESCRIPTION OF THE DRAWINGS

In drawings which depict non-limiting embodiments of the invention:

FIG. 1 schematically depicts a method for detecting a POG in threedimensions according to a particular embodiment of the invention;

FIG. 2 is a schematic illustration of the geometry involved in the FIG.1 method for using a pair of LOS vectors to determine a POG inthree-dimensions;

FIG. 3 is a schematic diagram of a system for performing the method ofFIG. 1 in accordance with a particular embodiment of the invention;

FIG. 4 schematically depicts a particular embodiment of a method forobtaining image data containing image(s) of the user's eyes andextracting, from the image data, a number of characteristics which maybe used to determine the LOS vectors used in the method of FIG. 1;

FIG. 5 is an example of regions of interest within the image data thatare suitable for use with the method of FIG. 4;

FIG. 6 schematically depicts a particular embodiment of a method forobtaining rough pupil characteristics from the image data suitable foruse with the method of FIG. 4;

FIG. 7 schematically depicts a particular embodiment of a method forobtaining pupil glint information from the image data suitable for usewith the method of FIG. 4;

FIG. 8 schematically depicts a particular embodiment of a method forobtaining fine pupil characteristics from the image data suitable foruse with the method of FIG. 4;

FIG. 9 schematically depicts a particular embodiment of a method forusing the multiple glint and fine pupil information obtained in themethod of FIG. 4 to estimate the LOS vector for one of the user's eyes;

FIG. 10 is a schematic illustration of the geometry involved in the FIG.9 method of using the multiple glint and fine pupil information todetermine the LOS vector;

FIG. 11 schematically illustrates a method for obtaining calibrationvalues which may be used to calibrate or otherwise adjust the POGestimate obtained using the method of FIG. 1;

FIG. 12 schematically illustrates a method for applying weights to thecalibration values determined in FIG. 11 and for adjusting the POGestimate obtained using the method of FIG. 1 using the weightedcalibration values;

FIG. 13 schematically depicts a method for applying moving averagefilters to various parameters of the FIG. 1 method;

FIG. 14 schematically illustrates the determination of an i^(th)calibration parameters for the first eye;

FIG. 15 schematically illustrates the application of weightedcalibration parameters to the uncalibrated LOS vector for the first eye;

FIG. 16 schematically depicts an arrangement of lighting suitable foruse with the FIG. 3 system according to one particular exemplaryembodiment of the invention;

FIGS. 17A and 17B schematically illustrate a method for mapping detectedoff-axis glints to corresponding off-axis lights according to aparticular embodiment of the invention; and

FIGS. 18A-18E schematically depict reference points and detected glintsin the various iterations of the FIG. 17B mapping method.

DETAILED DESCRIPTION

Throughout the following description, specific details are set forth inorder to provide a more thorough understanding of the invention.However, the invention may be practiced without these particulars. Inother instances, well known elements have not been shown or described indetail to avoid unnecessarily obscuring the invention. Accordingly, thespecification and drawings are to be regarded in an illustrative, ratherthan a restrictive, sense.

Particular aspects of the invention provide methods and apparatus forestimating the POG of a user in three dimensions. One aspect of theinvention provides a method for determining point-of-gaze (POG) of auser in three dimensions wherein the method comprises: presenting athree-dimensional scene to both of the eyes of the user; capturing imagedata which includes image(s) of both eyes of the user; estimating firstand second line-of-sight (LOS) vectors in a three-dimensional coordinatesystem for the user's first and second eyes based on the image data; anddetermining the three-dimensional POG in the three-dimensionalcoordinate system using the first and second LOS vectors. In someembodiments, the three-dimensional scene presented to both of the user'seyes is the real world and the three-dimensional coordinate system is asystem for identifying the location of point(s) or regions or the likein the real world.

FIG. 1 schematically depicts a method 100 for estimating the POG of auser in three dimensions according to a particular embodiment of theinvention. Method 100 begins in block 105 which involves presenting athree-dimensional scene to both eyes of the user. The same singlethree-dimensional scene may be presented to both of the user's eyes. Insome embodiments, the three-dimensional scene is the real world andthere is no need to actively present the scene to both of the user'seyes as the user's eyes automatically take-in the real world scene. Inother embodiments, the three-dimensional scene presented to both of theuser's eyes is a three-dimensional scene created or otherwise generatedby a scene generation system. By way of non-limiting example, a scenegeneration system may comprise: a 3D volumetric display, a holographicdisplay, a parallax display or the like.

Block 110 involves capturing image data. The image data captured inblock 110 comprises image(s) of both of the eyes of a user. The imagedata for each eye may be captured simultaneously or at different times.The image data for each eye may be captured using the sameimage-capturing device(s) or using separate image-capturing device(s)for each eye. Block 110 may also involve processing the image data suchthat the image data may be used to calculate LOS vectors for each of theuser's eyes. A particular embodiment for capturing image data isexplained in more detail below. In general, however, the capturing ofimage data in block 110 may be accomplished using any suitableimage-capturing technique.

Once the image data is captured in block 110, method 100 proceeds toblock 120 which involves using the block 110 image data to determine aLOS vector ( LOS ₁) for a first of the user's eyes. The block 120 LOSvector LOS ₁ represents an estimate of the direction of theline-of-sight of a first one of the user's eyes and is based on one ormore characteristics of the eye (e.g. position and/or orientation)ascertained from the block 110 image data. A particular embodiment fordetermining LOS ₁ is explained in more detail below. In general,however, determining the block 120 LOS vector LOS ₁ may be accomplishedusing a variety of suitable techniques. It is assumed that the block 120LOS vector LOS ₁, originates from the center of the cornea of the user'sfirst eye (CC₁). Block 120 may also involve estimating the location inspace of the corneal center CC₁ of the user's first eye based on one ormore characteristics of the eye ascertained from the block 110 imagedata.

Block 130 involves using the block 110 image data to determine a secondLOS vector ( LOS ₂) representing an estimate of the direction of theline-of-sight of a second one of the user's eyes based oncharacteristics of the eye ascertained from the block 110 image data.Block 130 may also involve determining a spatial location of the cornealcenter CC₂ of the user's second eye based on the block 110 image data.Block 130 may be similar to block 120, except that block 120 involvesthe second one of the user's eyes.

After determining the LOS vectors LOS ₁, LOS ₂, method 100 proceeds toblock 140 which involves determining the user's three-dimensional POGbased on the LOS vectors LOS ₁, LOS ₂. FIG. 2 schematically depicts thegeometry of the block 140 POG determination in accordance with aparticular embodiment of the invention. As discussed above, blocks 120,130 involve determining the LOS vectors LOS ₁, LOS ₂ and the cornealcenters CC₁, CC₂ for each of the user's eyes 202A, 202B. In general, theLOS vectors LOS ₁, LOS ₂ may be extended as lines in three-dimensionalspace; however, the lines corresponding to LOS vectors LOS ₁, LOS ₂ willnot necessarily intersect one another. That is, the lines correspondingto the LOS vectors LOS ₁, LOS ₂ may be skewed.

Blocks 142 and 144 schematically depict one possible embodiment fordetermining a three-dimensional POG using the LOS vectors LOS ₁, LOS ₂and the corneal centers CC₁, CC₂ determined in blocks 120, 130. Block142 involves determining a vector W which is the shortest possiblevector that intersects the lines corresponding to both LOS ₁ and LOS ₂.By defining the vector W in this way, W will be orthogonal to both LOS ₁and LOS ₂ or, equivalently:

LOS₁ · W = LOS₂ · W=0  (1)

where · represents the dot product operator.

The two points P(s) and Q(t) are defined to be the points at which Wrespectively intersects the lines extending from LOS vectors LOS ₁ andLOS ₂. Based on these definitions, it may be seen from FIG. 2 that:

W=[P(s)−Q(t)]=[( CC ₁+s LOS ₁)−( CC ₂+t LOS ₂)]  (2)

where CC ₁ and CC ₂ are vectors between the origin and the locations ofthe corneal centers CC₁, CC₂ in three dimensions and s and t are unknownscaling parameters.

Substituting (2) into (1) yields a pair of equations in terms of thescaling parameters s, t:

s( LOS₁ · LOS₁ )−t( LOS₁ · LOS₂ )= LOS₁ ·( CC₂ − CC₁ )  (3)

s( LOS₁ · LOS₂ )−t( LOS₂ · LOS₂ )= LOS₂ ·( CC₂ − CC₁ )  (4)

With the exception of the scaling parameters s, t, the quantities inequations (3) and (4) are known from blocks 120 and 130. Block 142 mayinvolve solving equations (3) and (4) (or equivalent equations) for thescaling parameters s and t and then using these scaling parameters s andt to compute W according to equation (2).

In the illustrated embodiment, block 144 involves determining themidpoint of the vector W to be the current estimate of thethree-dimensional POG. After obtaining an estimate of the current POG inthree-dimensions, method 100 may loop back to block 105, where theprocess may be repeated to continually track the user's POG in threedimensions.

Even where a human user's eyes are fixated at a POG in athree-dimensional scene (i.e. the POG is imaged onto the fovea of theuser's eyes), the user's eyes exhibit a number of movements. Typically,a fixation lasts between from 200-600 ms and will encompass around 1° ofvisual angle. While fixating, the eye will drift with typical amplitudeson the order of 0.1° of visual angle and frequencies on the order of 2-5Hz. This drift is typically compensated by microsaccades which are fastshifts in eye orientation with amplitudes on the same order as theamplitudes of the drift. Superimposed on the drift and the microsaccadesare tremor movements, with typical amplitudes around 0.008° of visualangle and frequency components typically ranging from 30-150 Hz.

Without wishing to be bound by theory, it is thought that these smalleye motions during fixation enable the sensors in the eye to becontinually refreshed. The human brain subconsciously compensates forthese small eye movements which occur during fixation. Consequently,humans are able to concentrate on a specific fixation without perceivingthe small eye movements. However, these small eye movements caninterfere with the precision or accuracy of LOS determination. Theresultant inaccuracies can be compounded when a pair of LOS vectors areused to determine a three-dimensional POG. Consequently, it is desirableto include procedures in method 100 to accommodate these small eyemovements while minimizing the impact on the three-dimensional POGdetermination.

In addition to these small eye movements which occur during fixation,the human eye exhibits saccades which are relatively large motions ofthe eye used to reorient the fovea to another area of interest. Saccadesmost ofter range from 1°-40° of visual angle and last between 30-120 mswith a delay in a typical range of 100-200 ms between saccades. Duringsaccades, both of a user's eyes do not necessarily move in unison andthe sensitivity of both eyes to visual input may be reduced.Furthermore, for the brain to register a true conscious POG, the scenewhich includes the POG is focused on the retina of the user's eye. Theprocess by which the ciliary muscles compress or expand the lens in theeye to change its focal depth is referred to as accommodation. In manyapplications, it is not desirable to estimate a POG (or at least to usePOG estimation information) during saccades, as such POG estimations donot correspond to conscious POG positions in the user's brain.

Further to all of the movements of the user's eyes, the user may movehis or her body and more particularly, his or her head. Head movementscan make it difficult to determine the LOS of the user's eyes. Thesedifficulties can be exacerbated when a pair of LOS vectors are used todetermine a user's POG in three dimensions. Consequently, it isdesirable to include procedures in method 100 to accommodate these headmovements while minimizing the impact on the three-dimensional POGdetermination.

FIG. 3 schematically depicts an apparatus 210 for estimating the POG ofa user in three dimensions according to a particular embodiment of theinvention. FIG. 3 shows that apparatus 210 comprises an imaging system219 that is located, or otherwise configured, to view a user 212 (or atleast his or her eyes). In the illustrated embodiment, imaging system219 includes optics 214 (which may comprise one or more lenses and/ormirrors), an optical filter 216 and an image sensor 218. Imaging system219 has an optical axis 213 and a field of view 215. Although the useris permitted to have head movement, the user's head is preferablyoriented such that his or her eyes are in the field of view 215 ofimaging system 219. In some embodiments, apparatus 210 may include aplurality of imaging systems 219 so as to view user 212 from multiplelocations. The use of a plurality of imaging systems 219 facilitates acombined field of view which can permit user 212 to have a wider rangeof motion. The fields of view 215 of these imaging systems 219 may beoverlapping or non-overlapping. In some embodiments, the inclusion ofmultiple imaging systems 219 may be used to capture images of each ofthe user's eyes separately.

In general, it is desirable for image sensor 218 to have a relativelyhigh resolution and a relatively high frame rate, provided thatcontroller 220 is able to accommodate such resolution and frame rate asdescribed below. Increases in resolution of image sensor 218 allowapparatus 210 to accommodate a larger range of head motion with theuser's eyes remaining in the field of view of sensor 218 while stilloutputting image data with sufficient spatial resolution to accuratelydetermine the LOS vectors of the user's eyes as discussed in more detailbelow. The resolution of image sensor 218 may (but need not necessarily)be on the order of 640×480 pixels or greater. Increases in the framerate of image sensor 218 allow apparatus 210 to accommodate faster headand eye movement without losing LOS tracking of the user's eyes. Theframe rate of image sensor 218 may (but need not necessarily) be on theorder of 30 Hz or greater. In some embodiments, image sensor 218 may beimplemented by a camera which may include its own control componentsand/or I/0 components (not explicitly shown). Image sensor 218 may (butneed not necessarily) be digital.

Apparatus 210 incorporates optics 214. Optics 214 may comprise one ormore lenses, minors and/or other optical components. Optics 214 beadjusted depending on the relative location of the eyes of user 212.Optics 214 may be used in some applications to adjust the image of user212 which reaches image sensor 218. In some applications, optics 214 maybe controlled by imaging system 219 and/or control components associatedwith imaging system 219.

Apparatus 210 incorporates lighting 224 for illuminating user 212. Incurrently preferred embodiments, lighting 224 operates at infrared (IR)wavelengths (e.g. 800 nm-1000 nm). Light at these wavelengths isinvisible and therefore does not distract user 212. In addition,fluorescent lights, which form the ambient light sources in the currentdevelopment environment, exhibit low light intensities in this spectralrange. Consequently, the performance of apparatus 210 can be maderelatively insensitive to ambient light effects by including optionaloptical filter 216 which passes IR light, but which blocks light in thevisible spectrum. In general, lighting 224 may operate at otherwavelengths and optical filter 216 may be selected to pass light at thewavelength of lighting 224 and to attenuate light at other wavelengths.In one particular embodiment, lighting 224 comprises a plurality of LEDswhich produce light at approximately 880 nm. Groups of such LEDs may beclosely packed together to approximate point light sources.

In some embodiments, the physical arrangement of lights in lighting 224(not explicitly shown in FIG. 3) can be used to help extract thefeatures of interest from within the image data captured by imagingsystem 219. More particularly, one or more LEDs in lighting 224 may beplaced off of the optical axis 213 of imaging system 219 to provideoff-axis lighting 224A and one or more LEDs in lighting 224 may beplaced as close as reasonably possible to optical axis 213 to provideon-axis lighting 224B. For the sake of clarity only, in the schematicillustration of FIG. 3, off-axis lighting 224A and on-axis lighting 224Bare not shown in their actual positions relative to imaging system 219.In one particular embodiment, off-axis lighting 224A is provided by aplurality of groups of LEDs spaced apart (circumferentially orotherwise) around optical axis 213 and on-axis lighting 224B is providedby a plurality of LEDs placed as close as reasonably possible to opticalaxis 213. Each spaced-apart group of off-axis LEDs may include one ormore individual LEDs depending on illumination requirements.

FIG. 16 schematically depicts an arrangement of lighting 224 accordingto one particular exemplary embodiment. As discussed above, off-axislights 224A are located away from optical axis 213 and may comprise aplurality of groups of LEDs (or other light source(s)) which approximatepoint sources. In the illustrated embodiment of FIG. 16, off-axis lights224A comprise five groups of LEDs which approximate five correspondingpoint source approximations 224A-1, 224A-2, 224A-3, 224A-4, 224A-5.Point source approximations 224A-1, 224A-2, 224A-3, 224A-4, 224A-5 maybe collectively and individually referred to herein as off-axis lights224A. In general, there may be a different number of off-axis lights224A, although two or more off-axis lights 224A are required for LOSvector calculation according to the model-based method described hereinand three or more off-axis lights 224A may be used to provide redundancyagainst the loss or corruption of off-axis glints due to eye movement,as described in more detail below. In the illustrated embodiment, onaxis lights 224B comprise a group of LEDs (or other light source(s))which are located at or near optical axis 213.

In the illustrated embodiment, off-axis lights 224A and on-axis lights224B are provided by groups of LEDs and each group comprises a pluralityof light sources. This is not necessary, each group may contain lightsources other than LEDs and may contain a single light source. In someembodiments, apparatus 210 may comprises multiple sets of lighting 224,off-axis lights 224A and/or on-axis lights 224B. In some embodiments,there may be a one-to-one correspondence between lighting 224 andimaging systems 219 (i.e. one set of lighting 224 for each imagingsystem). In other embodiments, there may be multiple lighting systems224 to service a single imaging system 219. In still other embodiments,each imaging system 219 may comprise its own corresponding on-axislighting 224B and multiple imaging systems 219 may share the sameoff-axis lighting 224A.

Due the retro-reflectivity of the user's retina, on-axis light 224B thatenters the eye and strikes the retina is typically reflected back towardimaging system 219 on or near optical axis 213 and results in imageswhere the user's pupil appears relatively bright. Images obtained usingonly on-axis components 224B of lighting 224 may be referred to as“bright pupil” images. Images obtained using only the off-axiscomponents 224A of lighting 224 are not retro-reflected along opticalaxis 213 and therefore do not illuminate the user's pupil in the samemanner. Images obtained using only the off-axis components 224A oflighting 224 may be referred to as “dark pupil” images. Off-axis lights224A result in Purkinje reflections (more commonly referred to as“glints”) from the corneal surface which appear in the resultant darkpupil images. In particular embodiments, obtaining dark pupil imagesinvolves activating any two or more of the groups of off-axis lights224A to obtain two or more corresponding glints. The particular groupsof off-axis lights 224A selected may depend on the quality of glintsthat they produce. As explained in more detail below, the bright pupiland dark pupil images can be used to help distinguish the user's pupilfrom the user's iris within the captured images and can help to locateglints within the captured images.

Apparatus 210 is controlled by controller 220. Controller 220 maycomprise one or more programmable processor(s) which may include,without limitation, embedded microprocessors, computers, groups of dataprocessors or the like. Some functions of controller 220 may beimplemented in software, while others may be implemented with specifichardware devices. The operation of controller 220 may be governed byappropriate firmware/code residing and executing therein, as is wellknown in the art. Controller 220 may comprise memory or have access toexternal memory. In one particular embodiment, controller 220 isembodied by a computer, although this is not necessary, as controller220 may be implemented in an embedded architecture or some other controlunit specific to apparatus 210. Controller 220 may comprise or mayotherwise be connected to other interface components (not explicitlyshown) which may be used to interact with any of the other components ofapparatus 210. Such interface components will be understood by thoseskilled in the art.

In the illustrated embodiment, apparatus includes electronics 222 whichare used by controller 220 to synchronize the operation of imagingsystem 218 and lighting 224 and, in some embodiments, to control whichof off-axis lights 224A are active. Synchronization of image sensor 218and lighting 224 may involve alternatively: activating one or moreoff-axis lights 224A and activating imaging system 218 for a period oftime in which off-axis lights 224A are activated to capture a dark pupilimage; and activating one or more on-axis lights 224B and activatingimage sensor 218 for a period of time in which on-axis lights 22B areactivated to capture a bright pupil image. In particular embodiments,the activation of image sensor 218 may be controlled by a shutter or thelike (not explicitly shown).

Particular embodiments of the functional blocks of method 100 are nowdescribed in more detail. Operational details of processes similar tothe functional operation of some of the method 100 processes aredescribed for one eye in C. Hennessey, “Eye-gaze tracking with free headmotion,” Master's thesis, University of British Columbia, August 2005(Hennessey), which is hereby incorporated herein by reference and whichis hereinafter referred to as “Hennessey”.

As discussed above, block 110 involves capturing image data for theuser's first and second eyes. A particular embodiment of a method 300for capturing this image data is depicted in FIG. 4. Method 300 beginsin block 302 which involves capturing raw image data that includes abright pupil image and a dark pupil image for each eye. In particularembodiments, both eyes are captured in a single image. That is, a singleblock 302 bright pupil image contains bright pupil images of both eyesand a single block 302 dark pupil image contains dark pupil images ofboth eyes. Simultaneous capture of the bright pupil and dark pupilimages for both eyes avoids inter-image disparity that may lead tospurious results for the LOS and POG estimates. However, simultaneouscapture of the bright pupil image for both eyes and the dark pupil imagefor both eyes is not necessary. In some embodiments, bright pupil anddark pupil images may be obtained separately for each eye.

The raw images obtained in block 302 may include all of the datarecorded by image sensor 218 (FIG. 3). In the first iteration of method300 or when the user exhibits substantial head movement, it may bedesirable to use the entirety of the recorded image data. In subsequentiterations, however, it is desirable to reduce the amount of block 302image data used in the remainder of method 300, since this will improvethe looping speed of method 100 (FIG. 1). On the second and subsequentiterations of method 300, the block 302 image data recorded by imagesensor 218 is reduced to a portion of the image data recorded in block304. The block 304 portion of image data may be referred to as a “regionof interest” (ROI) and particular block 304 ROIs may be ascertained fromthe centers of the user's pupils. As discussed in more detail below, thecenters of the user's pupils may be determined in a previous iterationof method 100.

Particular examples of suitable ROIs are shown in FIG. 5. In the FIG. 5example, the entire image captured by image sensor 218 is shownschematically as the hashed region 240 and the centers of the user'spupils (within image 240) determined in the previous iteration of method100 are shown as C₁ and C₂. In one of the FIG. 5 embodiments, the block304 ROI 242 is determined to include image data that is: spaced apartfrom the uppermost pupil center (C₂ in the illustrated example) by adistance h₁; spaced apart from the rightmost pupil center (C₂ in theillustrated example) by a distance w₂; spaced apart from the bottommostpupil center (C₁ in the illustrated example) by a distance h₂; andspaced apart from the leftmost pupil center (C₁ in the illustratedexample) by a distance w₁. In some embodiments, h₁=h₂ and w₁=w₂. In someembodiments, w₁ and w₂ need not be used and ROI 242 may include theentire width of the image data captured by image sensor 218.

In the other FIG. 5 embodiment, the block 304 ROI 244A, 244B(collectively 244) is determined to include a first region 244Asurrounding the first pupil center C₁ and a second region 244Bsurrounding the second pupil center C₂. In the illustrated embodiment,regions 244A, 244B are rectangles centered at C₁ and C₂. However, thisis not necessary. In other embodiments, regions 244A, 244B may haveother shapes, such as circular shapes, for example, and need not becentered on pupil centers C₁, C₂. The block 304 process of obtaining aROI may be implemented in hardware and/or software. In some embodiments,imaging system 219 (FIG. 3) may determine the ROI (at least in part)prior to outputting the image data (i.e. blocks 302 and 304 of method300 may be performed together by imaging system 219). In otherembodiments, controller 220 (FIG. 3) may comprise or have access tosoftware which determines the ROI (at least in part) on the basis ofsome or all of the image data captured by imaging system 219. In someembodiments, a first ROI (e.g. ROI 242) may be implemented in hardwareand a second ROI (e.g. ROI 244) may be implemented in software. In someembodiments, depending on processing power, a ROI is not required, andblock 304 may be skipped.

In the remaining discussion of method 300 (FIG. 4), it is assumed thatreferences to image data refer to the subsets of image data obtained bythe block 304 ROI process. In addition, while not explicitly shown,subsequent processing in method 300 is performed on the image data foreach of the user's eyes. The method 300 data extracted for each eye areused to estimate the LOS for each of the user's eyes as discussed inmore detail below.

Referring again to FIG. 4, block 306 involves obtaining approximatepupil characteristics (referred to herein as the “rough pupil”). Amethod 350 for the block 306 rough pupil extraction according to aparticular embodiment of the invention is shown in FIG. 6. Method 350begins in optional block 352, which involves applying a smoothing filter(e.g. a Gaussian filter) to the bright pupil and dark pupil image datato smooth the image data and to reduce image noise. Block 354 involvessubtracting the dark pupil image from the bright pupil image (i.e. theintensities of the pixels of the dark pupil image from the intensitiesof corresponding pixels of the bright pupil image) to produce adifference image. The difference image highlights the pupil (which isbright in the bright pupil image due to on-axis lights 224B and dark inthe dark pupil image) and helps to segment the pupil from the iris.

Block 356 involves separating the pixels corresponding to the relativelybright pupil from the pixels corresponding to the relatively darkbackground in the difference image. In particular embodiments, the block356 pupil pixel separation involves creating a bimodalintensity/brightness level histogram using the difference image, whereinthe first mode of the histogram reflects the dark background and thesecond mode of the histogram reflects the bright pupil. A thresholdingprocess may then be used to make a binary separation of the pixelscorresponding to the relatively bright pupil mode from the pixelscorresponding to the relatively dark background mode. For example,pixels of the difference image which are within a threshold intensityregion (e.g. determined from the second mode of the histogram) may bedetermined to be part of the pupil and may be assigned a binary value(e.g. 1) and pixels outside of this threshold intensity region may bedetermined to not be part of the pupil and maybe assigned a differentbinary value (e.g. 0).

Block 358 involves determining a pupil contour. The largest contourremaining after the binary thresholding process of block 356 is likelyto be the pupil. In one embodiment, block 358 involves identifying thecontour with the largest number of pixels that is within a shape thatcorresponds with known pupil characteristics. Such a shape may be anelliptical shape, for example, and the pupil characteristics may be aratio of the major ellipse axis to the minor ellipse axis that isrelatively close to unity (i.e. an elliptical shape that is somewhatclose to circular). A rejection test may be performed in block 358 toreject contours that do not qualify as pupils. In some embodiments, thisrejection test may involve computation of an isoperimetric quotient. Forexample, an isoperimetric quotient Q may be defined as

$Q = \frac{4\; \pi \; A}{p^{2}}$

where A is the area of the contour and p is the perimeter of thecontour. The quotient Q is equal to unity for a circle and decreases forobjects with a larger perimeter to area ratio. A threshold (e.g. Q=0.8)may be set such that if Q is less than the threshold, the contour isrejected. In another example, an ellipsoidal thresholding test may beconstructed wherein the width and height of the contour must be withinthreshold windows for the contour to qualify. Although not explicitlyshown in FIG. 4 or 6, if the detected pupil is rejected in block 358(i.e. the detected pupil fails the rejection test), then imageprocessing algorithm 300 may exit and return to block 302 to obtainfurther image data. Block 360 involves applying an ellipse fittingalgorithm to boundary points of the block 358 pupil contour. Inparticular embodiments, the block 360 ellipse fitting technique involvesa least squares optimization. In other embodiments, the block 360ellipse fitting algorithm may comprise some other suitable curve fittingtechnique. The center of the block 360 ellipse equation may be used asthe center of the pupil for further processing and for subsequentiterations of method 300.

Returning to method 300 (FIG. 4), block 308 involves an inquiry intowhether glasses have been detected. Eyeglasses generate brightreflections which may interfere with the pupil identification process.Block 308 may involve comparing the average intensity of the pupilpixels in the bright pupil image with a threshold brightness level.Reflections from eyeglasses are typically brighter than reflections fromthe retina. If the average intensity of the bright pupil image pixelsdetermined in block 306 to be part of the “pupil” is above thisthreshold brightness level, then it may be concluded (block 308 YESoutput) that this “pupil” is actually a reflection from eye glassesrather than a true pupil. If on the other hand the average intensity ofthe bright pupil image pixels determined in block 306 to be part of thepupil is below this threshold brightness level, then it may be concluded(block 308 NO output) that glasses have not adversely impacted the block306 rough pupil detection.

If it is concluded in block 308 that the user is wearing glasses (block308 YES output), then method 300 proceeds to block 312 which involvesdetermining which pixels in the bright pupil image have intensity valueshigher than the block 308 threshold and setting the intensity values ofthese pixels to zero. Method 300 then proceeds to block 314 to obtainanother set of rough pupil characteristics. Block 314 may besubstantially similar to the block 306 process for obtaining the roughpupil characteristics, except that the image data has been altered byremoval of the high intensity pixels reflected from the glasses from thebright pupil image.

As discussed above, where valid rough pupil characteristics are notdetermined (e.g. in block 308 or block 314), then method 300 may returnto block 302 to obtain more image data. Where method 300 exits andreturns to block 302 prior to completing on a number of subsequentiterations (e.g. due to invalid rough pupil characteristics), controller220 may cause method 300 to operate using the entire images captured byimage sensor 218 (rather than the images as reduced by the block 304 ROIprocess). This may help to resolve the issue of an eye moving outside ofthe ROI.

After obtaining valid rough pupil characteristics (in block 308 or block314), method 300 proceeds to block 318. Block 318 involves obtainingpupil glint information from the bright pupil image. This pupil glintinformation can be used to refine the detected pupil characteristics inblock 320 as explained in more detail below. FIG. 7 depicts a method 370for the block 318 process of extracting pupil glint information from thebright pupil image in accordance with a particular embodiment of theinvention. As discussed above, the bright pupil image is obtained usingon-axis lights 224B. On-axis lights 22B create a corresponding glint(i.e. corneal reflection of on-axis lights 224B) in the bright pupilimage. In general, method 370 for extracting the pupil glint from thebright pupil image is based on the realization that the glint created byon-axis lights 224B represents the brightest intensity pixels in thebright pupil image.

Method 370 commences in block 372, where a mask is applied to the brightpupil image data to reduce the possibility of mistakenly interpreting aglint located on the sclera (i.e. rather than the cornea). In oneparticular embodiment, the block 372 mask is centered at the center ofthe rough pupil (as determined in block 306 or 314 as the case may be)and may be circular or some other suitable shape. The dimensions of theblock 372 mask may be selected such that the mask is roughly the size ofthe user's iris. These dimensions may be selected based on knownpopulation averages or may be measured on a per user basis, for example.After applying the block 372 mask, block 374 involves locating the pixelhaving the highest intensity value in the resultant masked bright pupilimage data. The block 374 pixel detection may involve a maximum functionoperating on the pixel intensity values.

Block 376 involves computing an average intensity of the block 374highest intensity pixel and pixels surrounding the block 374 highestintensity pixel. In one particular embodiment, the pixels selected forthe block 376 intensity average include the block 374 highest intensitypixel and the eight pixels immediately surrounding the block 374 highestintensity pixel. These eight surrounding pixels may include the twohorizontal neighbors of the highest intensity pixel, the two verticalneighbors of the highest intensity pixel and the four diagonal neighborsof the highest intensity pixel.

In other embodiments, other groups of pixels in a vicinity of the block374 highest intensity pixel may be selected for the block 376 averagingprocess. Block 378 involves determining a threshold intensity value onthe basis of the block 376 average calculation. The block 378 thresholdintensity value may be less than the block 376 average by anexperimentally determined percentage. This threshold intensity is thenapplied to the masked bright pupil image data, resulting in a binarizedimage which distinguishes pixels having intensities above the block 378threshold and pixels having intensities below the block 378 threshold.Pixels having intensities above the block 378 threshold are determinedto be part of the bright pupil glint.

Block 380 then involves determining which contour in the resultantbinary image is the bright pupil glint. The block 380 process mayinvolve searching all of the contours in the binary image to locate ashape that meets a range of expected sizes for the bright pupil glint.In block 382, an ellipse is then fit to the block 380 glint contour.This ellipse fitting may be similar to the ellipse fitting in block 360.The center of the fitted ellipse may be determined to be the center ofthe bright pupil glint for the purposes of further processing.

Returning to method 300 (FIG. 4), after determining the bright pupilglint in block 318, method 300 uses the bright pupil glint to determinefine pupil characteristics in block 320. The block 320 fine pupilcharacteristics may involve obtaining an increased accuracydetermination of the block 306 rough pupil characteristics. FIG. 8depicts a method 400 for determining the block 320 fine pupilinformation according to a particular embodiment of the invention. Themethod 400 technique extracts the fine pupil characteristics from thebright pupil image. Method 400 begins in block 402 which involvesobtaining rough pupil information (determined in block 306 or 314) andpupil glint characteristics (determined in block 318). This informationobtained in block 402 may be used to provide masks in the subsequentprocedures of method 400. In particular embodiments, the block 306/314rough pupil obtained in block 402 may be the binarized rough pupil imageobtained in block 356 and the pupil glint obtained in block 402 may bethe binarized pupil glint image obtained after the block 378thresholding process.

In block 404, the block 318 pupil glint is expanded or dilated. Asexplained in more detail below, the dilation of the block 318 pupilglint ensures that the mask removes all of the pupil glint from thebright pupil image. In some embodiments, block 404 is not necessary. Thepresence of the block 404 dilation and the amount of dilation used inblock 404 may be dependent on the pixels used for the block 376averaging process and/or the threshold used in the block 378thresholding process to determine the block 318 pupil glint. Block 406involves inverting the binarized pixel values of the pupil glint (asoptionally dilated in block 404). In block 408, the result of the block406 pupil glint inversion is logically ANDed with the binarized roughpupil image to produce a mask. The effect of the block 408 AND operationis to remove the pixels corresponding to the bright pupil glint from thebinarized rough pupil image. The block 408 binary mask has a certainbinary value (e.g. 1) in the pixels corresponding to the rough pupil,except for those pixels in the block 318 bright pupil glint (asoptionally dilated in block 404) which have the opposing binary value(e.g. 0). The pixels outside the rough pupil also have the opposingbinary value (e.g. 0) in the block 408 mask.

Block 410 involves applying the block 408 mask to the original brightpupil image (after the above-described ROI operations, if present). Theresult of the block 410 masking process is an image where: (i) thepixels inside the block 408 mask (e.g. the pixels in the rough pupil butnot including those pixels in the bright pupil glint (as optionallydilated in block 404)) have intensity levels corresponding to thosecaptured in the bright pupil image of block 302; and (ii) the pixelsoutside the block 408 mask (e.g. the pixels in the bright pupil glint(as optionally dilated in block 404) and the pixels outside of the roughpupil) have their intensity levels reduced to zero. The result of theblock 410 masking process may be referred to as the “masked bright pupilimage”.

Block 412 involves computing the average of the intensity levels of thepixels in the masked bright pupil image. This average represents theaverage intensity of pixels that are in the rough pupil, but which arenot part of the high-intensity glint corresponding to on-axis lights224B. The block 412 average is used as the basis for determining athreshold level to be used in the subsequent procedures of method 400.Block 412 may involve reducing this average value by an experimentallydetermined percentage or an experimentally determined offset todetermine the threshold level. In other embodiments, the block 412threshold value may be based on some other function of the average ofthe intensity level of the pixels in the masked bright pupil image.

The block 412 threshold is applied to the unmasked bright pupil image inblock 414 to provide a binarized output. Pixels in the unmasked brightpupil image having intensity levels higher than the block 412 thresholdare assigned one binary value (e.g. 1) and pixels in the unmasked brightpupil image having intensity levels less than or equal to the block 412threshold are assigned the opposing binary value (e.g. 0). Pixelscorresponding to the pupil glint in the bright pupil image typicallyhave intensity values greater than those of the block 412 threshold andwill therefore be included in the resultant block 414 binarized brightpupil image.

Block 415 involves finding the boundary of the block 414 binarizedbright pupil image and setting the intensity values of those boundarypixels to one binary value (e.g. 1) and setting all of the other pixelsto the opposing binary value (e.g. 0). The result of block 415 istypically a binary outline of the pupil overlapped in part by a binaryoutline of the bright pupil glint. In block 416, the resultant block 415binary outline is logically ANDed with the block 406 inverted pupilglint to remove the glint from the block 415 binary outline. The resultof block 416 is a binary image having pixels with a first binary value(e.g. 1) on a portion of the outline of the pupil and the opposingbinary value (e.g. 0) in most other locations. This portion of theoutline of the pupil may be referred to as the “fine pupil contour”.

The block 416 binary image may have some spurious pixels that are nottruly on the fine pupil contour. Such spurious pixels may be generatedby noise or the like. Block 418 involves identifying the fine pupilcontour within the binary image resulting from block 416. In oneparticular embodiment, block 418 involves fitting a bounding box to eachcontour in the block 416 binary image and then determining the distancebetween the center of each bounding box and the center of the roughpupil ellipse (as determined in block 360). The contour whose boundingbox center is most proximate to the center of the rough pupil ellipse isthen identified as the fine pupil contour.

Block 422 involves fitting an ellipse to the fine pupil contour. Theblock 422 ellipse fitting may be similar to the ellipse fittingperformed in block 360 described above. The block 422 ellipse may beused to determine the LOS of the corresponding eye as discussed in moredetail below.

Returning to method 300 (FIG. 4), after determining the fine pupilcharacteristics in block 320, method 300 proceeds to block 322 whichinvolves determining the characteristics of two or more off-axis glintsfrom the dark pupil image. The block 322 off-axis glints may be createdby corneal reflection of off-axis lights 224A (see FIGS. 3 and 16)which, as discussed above, may be configured to approximate pointsources at locations away from optical axis 213. The characteristics ofthese off-axis glints may be used to calculate the LOS of thecorresponding eye as discussed in more detail below. The block 322procedure for determining the characteristics of the off-axis glintsfrom the dark pupil image is similar in many respects to method 370described above for detecting the pupil glint from the bright pupilimage.

The block 322 procedure for determining the characteristics of two ormore off-axis glints from the dark pupil image may differ from method370 in that the block 378 thresholding process is likely to reveal abinarized image having a plurality of glint candidates corresponding tothe plurality of off-axis lights 224A (see FIGS. 3 and 16) which areused to form the dark pupil image. Ellipses may be fit to each of thepair of glint candidates to determine their shapes and center locationsin a procedure similar to that of block 382 discussed above.

Method 300 then proceeds to block 323 which involves mapping the block322 off-axis glints to their corresponding off-axis light sources 224A(see FIGS. 3 and 16). For the model-based method of calculating the LOSvectors (described below), it is desirable to have at least two off-axisglints to facilitate triangulation of the cornea center CC inthree-dimensions. However, the block 322 off-axis glints may bedistorted or lost due to movement of the eye and/or movement of otherbody parts (e.g. head or neck) which result in movement of the eye. Forexample, the block 322 off-axis glints may be distorted or lost when theimages of off-axis lights 224A are located off the cornea (e.g. on thesclera) or even at or near the boundary between the cornea and thesclera. To provide redundancy (i.e. to make it more likely that theimages of at least two off-axis lights 224A will be present on thecornea), off-axis lights 224A may be arranged to provide three or moredistinct off-axis point source approximations (see point sourceapproximations 224A-1, 224A-2, 224A-3, 224A-4, 224A-5 of FIG. 16) whichresult in three or more corresponding off-axis glints.

Block 323 involves mapping the individual off-axis glints detected inblock 322 to the individual off-axis lights 224A. The block 323 mappingis desirable for determining the LOS vectors according to themodel-based technique described below. In embodiments which use only twooff-axis lights 224A, the block 323 mapping may be relatively simple andmay involve comparing the x and y pixel displacements of the block 322off-axis glints. However, in embodiments where the number of off-axislights 224A is three or more to provide redundancy against the loss orcorruption of the off-axis glints, the block 323 mapping may be morecomplex.

In one particular embodiment, the block 323 mapping between off-axisglints and off-axis light sources 224A may be performed using a patternmatching technique which attempts to match the block 322 off-axis glintsQ_(i) (i=1 . . . M) obtained in each iteration of method 300 to a set ofreference glints R_(j) (j=1 . . . N), for which the correspondencebetween reference glints R_(j) and off-axis light sources 224A is known.FIGS. 17A and 17B schematically depict a pattern matching method 600which may be used to match off-axis glints Q_(i) (i=1 . . . M) to a setof reference glints R_(j) (j=1 . . . N) according to a particularembodiment of the invention.

Pattern matching method 600 shown in FIGS. 17A and 17B involvesobtaining a reference glint pattern which includes glint data (e.g.glint locations) for a set of N valid reference glints R_(j) (j=1 . . .N) and mapping the individual reference glints R_(j) to theircorresponding off-axis light sources 224A. In the illustratedembodiment, this reference glint pattern R_(j) (j=1 . . . N) is obtainedand mapped in block 602 of FIG. 17A. An exemplary reference glintpattern R_(j) (j=1 . . . N) is shown schematically in FIG. 18A. In theillustrated example, N=3 and there are three reference glints R₁, R₂ andR₃.

Obtaining and mapping a reference glint pattern R_(j) (j=1 . . . N) inblock 602 may be performed in a first iteration of method 300 (FIG. 4)or in a different calibration iteration, for example. A user may beasked to position their head and/or to focus on a particular point (e.g.a central point) during the block 602 calibration. With the user's headand eyes oriented in a particular manner (i.e. configured forcalibration), the block 602 mapping between reference glints R_(j) andtheir corresponding off-axis lights 224A may be performed manually basedon user knowledge of the locations of off-axis lights 224A and thecorresponding reference glints R_(j). In some embodiments, the block 602mapping between reference glints R_(j) and their corresponding off-axislights 224A may be automated based on relationships between thelocations of off-axis lights 224A to corresponding reference glintsR_(j) when the user's head and eyes are configured for calibration. Byway of non-limiting example, if the head is configured for calibrationin block 602 such that all glints are detected (segmented from theimage) properly then the reference glints R_(j) can be mapped tooff-axis lights 224A based on spatial relationships between thereference glints R_(j) and off-axis lights 224A (i.e. the glint to theleft is the left light source, glint to the right is right light source,top most glint is the top most light source, etc.)

After obtaining the reference glint pattern R_(j) (j=1 . . . N) andmapping the individual glints R_(j) to corresponding off-axis lights224A in block 602, subsequent iterations of method 600 involve a patternmatching method which matches a pattern of off-axis glints Q_(i) (i=1 .. . M) obtained in block 322 (FIG. 4) to the reference glint patternR_(j) (j=1 . . . N) and thereby maps each of the detected off-axisglints Q_(i) to a corresponding off-axis light 224A. This patternmatching method is schematically depicted in FIG. 17B.

The FIG. 17B pattern matching method begins in block 604 which involvesgetting the off-axis glints Q_(i) (i=1 . . . M) obtained in block 322(FIG. 4) and determining which particular glint Q_(α) is closest to theimage pupil center (p_(c)). A particular exemplary illustration of theblock 604 process is shown schematically in FIG. 18B, where the block322 process has detected M=4 glints, Q₁, Q₂, Q₃, Q₄. The image pupilcenter p_(c) may be ascertained in block 306 or 320 (FIG. 4), asdiscussed above. Block 604 may involve evaluating the distances betweenthe individual glints Q_(i) and the image pupil center p_(c). In theexemplary illustration of FIG. 18B, the closest glint Q_(i) to the imagepupil center p_(c) is glint Q₂ and therefore block 604 would involvesetting Q_(α)=Q₂. In the illustrated embodiment, method 600 assumes thatthe closest glint Q_(α) to the image pupil center p_(c) is a validreference glint.

Method 600 then proceeds to block 606 which involves initializing afirst reference point counter j by setting j=1 and initializing a globalminimum distance d_(min,global)=∞. As discussed in more detail below,the reference point counter j allows method 600 to iterate through thereference points R_(j) (j=1 . . . N) and the global minimum distanced_(min,global) is a parameter used in the method 600 pattern matchingtechnique. Method 600 then proceeds to block 608, which involves aninquiry as to whether the first reference point counter j is greaterthan N. For the first iteration, the block 608 inquiry is negative andmethod 600 proceeds to block 610. Block 610 involves determining thetranslation T_(j) required to move glint Q_(α) to the location of Sincethe first reference point counter j was just initialized to j=/(block606), the first iteration of block 610 involves determining thetranslation T₁ required to move glint Q_(α) to the location of R₁.

Method 600 then proceeds to block 612 which involves applying the block610 translation T_(j) to the detected glints Q_(i) (i=1 . . . M). FIG.18C shows the block 612 application of the block 610 translation T_(i)for the exemplary embodiment for j=1 (i.e. the first iteration of blocks610 and 612). Comparing FIGS. 18B and 18C, it may be seen that Q_(α)=Q₂is translated to the location of R_(j)=R₁ and that the other glints Q₁,Q₃, Q₄ have been translated by an equivalent translation T₁.

After applying the block 612 translation to the detected glints Q_(i),method 600 involves cycling through the glints Q_(i) (i=1 . . . M) andmeasuring the distance between each glint Qi and the reference pointsR_(j) (j=1 . . . N) to detect distances which may be less than athreshold distance d_(thresh). This process begins in block 614 whichinvolves initializing a glint counter i. Block 614 may involve settingthe glint counter to i=1. Although only one inquiry is expresslyillustrated in FIG. 17B, block 616 may involve a pair of inquiries. Afirst inquiry involves inquiring as to whether i=α. If i=α, then block616 may increment the glint counter i by one and return to block 616. Itwill be appreciated that there is no need to compute distances for theglint Q_(α), as the glint Q_(α) has been translated directly to areference point R_(j). The second block 616 inquiry involves an inquiryas to whether the glint counter i is greater than M. For the firstiteration, the second block 616 inquiry is negative and method 600proceeds to block 618.

Block 618 involves computing the distances d_(i,k) between the currentglint Qi and the reference points R_(k) (k=1 . . . N, k≠j). The index kmay be referred to herein as the second reference point counter. It willbe appreciated that there is no need to compute distances for thereference point R_(k)=R_(j), as the glint Q_(α) has been translateddirectly to a reference point R_(j). The block 618 distance calculationsd_(i,k)=d_(1,2) and d_(i,k)=d_(1,3) are illustrated in dashed lines inFIG. 18C for glint Q₁ of the exemplary configuration.

Method 600 then proceeds to block 620 which involves evaluating whetherthe minimum distance MIN(d_(i,k)) determined in block 618 is the newglobal minimum distance d_(min,global). In the first iteration of block618, the global minimum distance d_(min, global) is infinity (asinitialized in block 606). As such, the minimum distance MIN(d_(i,k))determined in block 618 will be the new global minimum d_(min, global)and the block 620 inquiry is positive (i.e. block 620 YES output). Assuch, method proceeds to block 622 which involves updating the globalminimum d_(min, global) by setting d_(min, global)=MIN(d_(i,k)).Referring to FIG. 18C, it can be seen that the actual minimum distanceMIN(d_(i,k)) in the exemplary illustration d_(i,k)=d_(1,2). Accordingly,in the FIG. 18C exemplary illustration, block 622 involves updatingglobal minimum d_(min, global)=MIN(d_(i,k))=d_(1,2). Block 622 alsoinvolves assigning a mapping of Q_(α)→R_(j). That is, a mapping isestablished between glint Q_(α) and reference point R_(j). In theexemplary illustration of FIG. 18C, this block 622 mapping assignmentinvolves assigning a mapping of Q_(α)=Q₂→R_(j)=R₁.

Method 600 then proceeds to block 624 where the block 218 minimumdistance MIN(d_(i,k)) is compared to a threshold distance d_(thresh).The threshold distance may be experimentally determined or otherwisecalibrated such that when a block 218 distance d_(i,k) is determined tobe less than d_(thresh), the glint Q_(i) is highly likely to correspondto the reference point R_(k). As such, if the block 218 minimum distanceMIN(d_(i,k)) is less than d_(thresh) (block 624 YES output), then method600 proceeds to block 628 which involves assigning a mapping ofQ_(i)→R_(k) where the subscripts i, k refer to the same subscripts ofthe block 218 minimum distance MIN(d_(i,k)). That is block 628 involvesassigning a mapping of glint Q_(i) to reference point R_(k). In theillustrated example of FIG. 18C, the block 218 minimum distanceMIN(d_(i,k))=d_(1,2) is greater than d_(thresh) (block 624 NO output),so method 600 proceeds to block 626.

In block 626, the glint counter i is incremented by one (i.e. to i=2)before returning to block 616. As discussed above, block 616 involves aninquiry as to whether the glint counter i=α. Since i=α=2, block 616 willincrement the glint counter i again, such that i=3. Blocks 618-626 arethen repeated for the new glint Q_(i)=Q₃. Referring to FIG. 18C, it canbe seen that for the illustrated example, the distances (d_(i,k)=d_(3,2)and d_(i,k)=d_(3,3)) between glint Q₃ and reference points R₂ and R₃ aregreater than both the global minimum d_(min, global) and the thresholdd_(thresh). Consequently, no further mapping assignments are made in thei=3 iteration. The glint counter i is then incremented to i=4 and blocks618-626 are repeated again for the new glint Q_(i)=Q₄. Referring to FIG.18C, it can be seen that for the illustrated example, the distances(d_(i,k)=d_(4,2) and d_(i,k)=d_(4,3)) between glint Q₄ and referencepoints R₂ and R₃ are greater than both the global minimumd_(min, global) and the threshold d_(thresh). Consequently, no furthermapping assignments are made in the i=4 iteration.

When method returns to block 616 after the i=4 iteration, the block 616inquiry is positive (i.e. block 616 YES output), so method 600 proceedsto block 630, where the first reference counter j is incremented by onebefore proceeding to the block 608 inquiry. In the illustrated example,block 630 involves setting the first reference counter j to j=2. In theillustrated example, since j=2≦N=3, the block 608 inquiry is negative(block 608 NO output). Method 600 then involves repeating blocks 610-630for a second iteration where the glint Q_(α) (i.e. the glint closest tothe image pupil center p_(c)) is translated to the second referencepoint R_(j)=R₂. The translated glints (Q₁, Q₂, Q₃, Q₄) and theglint-to-reference distances (d_(1,1), d_(1,3), d_(3,1), d_(3,3),d_(4,1), d_(4,3)) for the second (j=2) iteration of the exemplaryillustration are shown in FIG. 18C. As can be seen from FIG. 18C, forthe illustrated example, the distance d_(4,1) is a new global minimumd_(min, global), so the block 620 inquiry will be positive for the i=4iteration and block 622 will involve replacing the previous Q_(α)mapping with a mapping of Q_(α)=Q₂→R_(j)=R₂. FIG. 18C also shows thatnone of the glint-to-reference distances (d_(1,1), d_(1,3), d_(3,1),d_(3,3), d_(4,1), d_(4,3)) for the second (j=2) iteration are less thanthe threshold d_(thresh). Consequently, method 600 does not make anyblock 628 assignments in the second (j=2) iteration.

After evaluating the distances (d_(1,1), d_(1,3), d_(3,1), d_(3,3),d_(4,1), d_(4,3)), method 600 returns to block 630, where the firstreference counter j is incremented by one before proceeding to the block608 inquiry. In the illustrated example, block 630 involves setting thefirst reference counter j to j=3. In the illustrated example, sincej=3≦N=3, the block 608 inquiry is negative (block 608 NO output). Method600 then involves repeating blocks 610-630 for a third iteration wherethe glint Q_(α) (i.e. the glint closest to the image pupil center p_(c))is translated to the third reference point R_(j)=R₃. The translatedglints (Q₁, Q₂, Q₃, Q₄) and the glint-to-reference distances (d_(1,1),d_(1,2), d_(3,1), d_(3,2), d_(4,1), d_(4,2)) for the third (j=3)iteration of the exemplary illustration are shown in FIG. 18D. As can beseen from FIG. 18D, the translation involves in the third (j=3)iteration represents the best pattern matching between the glints Q_(i)(i=1 . . . M) and the reference points R_(j) (j=1 . . . N).

In the i=1 iteration of blocks 618-628, it can be seen from FIG. 18Dthat, in the illustrated example, the distance d_(i,k)=d_(1,2)represents a new global minimum distance d_(min,global), so the block620 inquiry will be positive for the i=1 iteration and block 622 willinvolve replacing the previous Q_(α) mapping with a mapping ofQ_(α)=Q₂→R_(j)=R₃. Also, in the i=1 iteration, the block 624 inquiry ispositive (i.e. the minimum distance MIN(d_(i,k))=d_(1,2) is less thanthe threshold d_(thresh)), so method 600 enters block 628 where theglint Q_(i)=Q₁ is assigned to reference point R_(k)=R₂ corresponding toMIN(d_(i,k))=d_(1,2). That is, block 628 involves making an assignmentof Q_(i)=Q₁→R_(k)=R₂. Also, in the i=3 iteration, the block 624 inquiryis once again positive for the minimum distance MIN(d_(i,k))=d_(3,1)(i.e. the minimum distance MIN(d_(i,k))=d_(3,1) is less than thethreshold d_(thresh)), so method 600 enters block 628 where the glintQ_(i)=Q₃ is assigned to reference point R_(k)=R_(j) corresponding toMIN(d_(i,k))=d_(3,1). That is, block 628 involves making an assignmentof Q_(i)=Q₃→R_(k)=R₁.

Accordingly, in the illustrated example, at the conclusion of the third(j=3) iteration, method 600 has made the following mapping assignments:

Q₂→R₃—assigned in block 622 during the i=1 iteration;

Q₁→R₂—assigned in block 628 during the i=1 iteration; and

Q₃→R₁—assigned in block 628 during the i=3 iteration.

Since the mapping between the reference points R_(i) and the off-axislights 224A is known from block 602, these mapping assignments areequivalent to mappings between the detected off-axis glints Q₁, Q₂, Q₃and the off-axis lights 224A. When method 600 returns to block 630, thefirst reference counter j is again incremented. In the illustratedexample, the first reference counter j is incremented to j=4. Thus, whenmethod 600 returns to block 608, the block 608 inquiry is positive (i.e.N=3 in the illustrated example). As such, method 600 ends in block 632.

Method 600 shown in FIGS. 17A and 17B compensates for translation,distortion, addition and deletion of block 322 glints. For this reason,it may be desirable to set the block 322 glint-detection threshold(actually implemented in block 378 of method 370 (FIG. 7)) to arelatively low value, so as to accept false positive glints, which willultimately be rejected during method 600 pattern matching. Using patternmatching method 600, the presence of false positive glints may produce abetter result than rejecting a glint because it did not meet the block378 glint-detection threshold. Since the block 322 glints arereflections from an at least quasi-spherical surface (the cornea),rotation of the image pattern should not be present. In addition, bytuning the threshold value d_(thresh), method 600 may accommodatechanges in scale due to the depth of the user's eyes. The depth of theuser's eyes may change with head movement, for example. In someembodiments, off-axis lights 224A may be positioned at irregular (i.e.non-symmetrical) locations to avoid the possibility that patternmatching method 600 could map multiple patterns of imaged glints Qi tothe pattern of reference points R_(j).

After mapping the block 322 off-axis glints Q_(i) to off-axis lights224A in block 323 (FIG. 4), method 300 proceeds to block 324 whichinvolves an inquiry into whether or not the user is wearing glasses. Theblock 324 inquiry may involve a procedure similar to the block 308inquiry. In some embodiments, the block 324 inquiry may use the answerobtained from the block 308 inquiry or may depend on user input. If noeyeglasses are detected (block 324 NO output), then method 300 proceedsto block 330 where it loops back to block 302. On the other hand, ifeyeglasses are detected (block 324 YES output), then method 300 proceedsto block 326. Block 326 involves an inquiry as to whether method 300 canaccurately extract two or more glints from the dark pupil image in block322 and match these two or more glints to corresponding off-axis lights224A in block 323. If two or more pupil glints are detectable withoutissues in block 322 and matchable to off-axis lights 224A in block 323(block 326 YES output), then method 300 proceeds to block 330 where itloops back to block 302. On the other hand, if method 300 fails toobtain a pair of pupil glints in block 322 (block 326 NO output), thenthe off-axis lights 224A used for the LOS determination may be changedin block 328 before proceeding to block 330 and looping back to block302. In some embodiments, the block 326 inquiry may be modified suchthat a number of failed glint-detection or glint-matching iterations arerequired before a NO output is decided and the off-axis lights 224A arechanged in block 328.

As explained briefly above, the data extracted from method 300 are usedto determine the LOS for each of the user's eyes. In one particularembodiment a model-based approach is used to determine the LOS. FIG. 9is a schematic illustration of a method 440 for using the pupil andglint data determined in method 300 to estimate the LOS vector for aparticular eye. It will be appreciated that method 440 may be performedfor each of the user's eyes to determine the LOS vector for each eye.Some of the operational details of processes similar to the functionaloperation of the method 440 processes are described for determining the2-dimensional point-of-gaze (POG) for one eye in Hennessey, which, asdiscussed above, is incorporated herein by reference.

Method 440 involves using a model of the user's eye. The eye model isrepresented in a principal coordinate system which also includes thescene that is presented to the user. An eye model 500 suitable for usein method 440 is schematically illustrated in FIG. 10. In model 500, eye502 includes pupil 504, cornea 506 and fovea 510. In the illustratedmodel 500, cornea 506 is assumed to be spherical with a center CC and aradius r. In model 500, P_(c) represents the center of pupil 504 in theprincipal coordinate system and the parameter r_(d) represents thedistance between model pupil center P_(c) and CC (i.e. the distancebetween the centers of cornea 506 and pupil 504). The geometricalparameters CC, r, P_(c) and r_(d) of eye model 500 are represented in aprincipal coordinate system. Model 500 also incorporates a parameter n,which represents the index of refraction of the aqueous humor fluid.

In particular embodiments, the parameters n, r and r_(d) may be based onpopulation averages determined experimentally or otherwise. In otherembodiments, these parameters may be measured or otherwise determined orcalibrated on a per-user basis. The purpose of method 440 (FIG. 9) is todetermine the LOS vector ( LOS) for one of the user's eyes in theprincipal coordinate system. In accordance with model 500 of theillustrated embodiment, the uncalibrated LOS vector 508 is the line thatextends from the center CC of cornea 506 through the model pupil centerP_(c) of pupil 504. The line that extends from the center CC of cornea506 through the model pupil center P_(c) of pupil 504 is also referredto as the “optical axis” 508 of eye model 500. Uncalibrated LOS vector508 may then be calibrated to arrive at calibrated LOS vector 512. Asexplained in more detail below, calibration may help to correct for theoffset of fovea 510 from optical axis 508.

Method 440 begins in block 442 which involves using data from aplurality of glints to determine the parameter CC (i.e. thethree-dimensional location of the center of cornea 506 in the principalcoordinate system). In particular embodiments, the block 442determination comprises a geometrical calculation which makes use of:image information obtained in block 322 (FIG. 4) for at least a pair ofoff-axis glints from the dark pupil image; parameters of eye model 500(e.g. r); a camera model; known three-dimensional locations of off-axislights 224A; and the block 323 mapping between the block 322 off-axisglints and the off-axis lights 224A. In particular embodiments, theoff-axis glints used for the block 442 determination of the corneacenter CC are selected to be the two glints which are closest to theimage pupil center p_(c) provided that the block 323 mapping informationis known for these glints—see the exemplary illustration of FIG. 18B.These two glints closest to the image pupil center p_(c) may be referredto as the “selected” glints. In particular embodiments, the camera modelused in block 442 is a standard pinhole camera model which includescamera parameters of effective focal length f and critical point c_(p)(i.e. the center of the image plane). This pinhole camera model isexplained in Hennessey.

In one particular embodiment, the block 442 geometrical calculationinvolves a triangulation procedure. In this triangulation procedure, foreach of the selected glints, the following parameters are transformedfrom the principal coordinate system to a secondary coordinate system:the image location of the selected glint (as determined in block 322),the corresponding glint location on the surface of cornea 506, thelocation of cornea center CC in model 500 and the location of thecorresponding off-axis light source 224A which maps to the selectedglint. For each of the selected glints, the secondary coordinate systemis chosen such that these parameters are located on a single axialplane. Equations representing the location of the cornea center CC ineach of these secondary coordinate systems may be determinedgeometrically. However, when transformed back to the principalcoordinate system, the cornea center CC generated in each secondarycoordinate system must be the same. This constraint results in aover-defined set of non-linear equations expressed in terms of theunknown locations of the selected glints on corneal surface 506 in theircorresponding secondary coordinate systems. This system of equations maybe solved numerically using a number of computational techniques knownto those skilled in the art. One non-limiting example of a technique forsolving over-defined systems of non-linear equations is Newton's method(also known as the Newton-Raphson method). The cornea center CC can thenbe calculated on the basis of either of the estimated values for thelocations of the selected glints on the corneal surface in theircorresponding secondary coordinate systems. A particular technique forimplementing the block 442 geometrical calculation is described inHennessey. While particular embodiments make use of a pair of selectedglints, it will be appreciated that three or more glints may be“selected” for use in the block 442 procedure to determine the corneacenter CC.

After determining the location of the cornea center CC in block 442,method 440 proceeds to block 444 which involves determining the locationof the model pupil center P_(c) in the principal coordinate system. Inone particular embodiment, the block 444 pupil center determinationmakes use of the fine pupil characteristics obtained in block 320, thecamera model information and the parameters of eye model 500 (e.g. r,r_(d), n) to trace a ray from the center of pupil in the image data(i.e. the image pupil center p_(c)) to the model pupil center P_(c) ofthe pupil 504 in eye model 500. In such a ray tracing, it is assumedthat the model pupil center P_(c) of model 500 is imaged to the pupilimage center p_(c) of the block 320 fine pupil characteristics. Whenperforming this ray tracing, it is necessary to take into account therefraction of the ray at the surface of cornea 506 due to the index ofrefraction n in the aqueous humor fluid.

In some embodiments, it is desirable to trace rays from multiple pointsin the block 320 fine pupil image data as a part of block 444 to improvethe accuracy of the determination of model pupil center P_(c). Forexample, block 444 may involve tracing rays from a plurality of pointson a perimeter of the block 320 fine pupil in the image data into eyemodel 500 to determine perimeter points on pupil 504 of eye model 500.Block 444 may involve tracing one or more opposing pairs of perimeterfine pupil image points onto pupil 504 of eye model 500 in the principalcoordinate system and then calculating an average of the pupil perimeterpoints in the principal coordinate system to be the model pupil centerP_(c). Opposing pairs or of perimeter fine pupil image points may beselected as being angularly equidistant from the major and/or minor axesof the ellipse fit to the fine pupil data in block 422. In someembodiments, groups of other sizes (i.e. other than pairs) of perimeterpupil image points may be selected from locations equally angularlyspaced around the fine pupil ellipse. The number of perimeter pupilimage points which may be traced may depend on processing resources. Itwill be appreciated that using a larger number of perimeter pupil imagepoints will result in a more accurate result (e.g. less susceptibilityto noise), but is more computationally expensive. In some embodiments,the number of perimeter pupil image points used in block 444 is in arange of 2-20. A particular technique for implementing the block 444 raytracing is described in Hennessey.

After determining the model pupil center P, in block 444, method 440proceeds to block 446 which involves determining the uncalibrated LOSvector ( LOS) 508 for the particular eye in the principal coordinatesystem. In particular implementations, the block 446 determination ofuncalibrated LOS 508 makes use of the cornea center CC (from block 442)and the model pupil center P_(C) (from block 444). In one particularembodiment, the block 446 determination of uncalibrated LOS 508 involvestracing a ray from the cornea center CC through the model pupil centerP_(c). The block 446 uncalibrated LOS 508 represents the LOS vector forone of the user's eyes and may used in block 120 or block 130 of method100 (FIG. 1) to determine the user's three-dimensional POG, as discussedin detail above.

Improved results may be obtained by applying calibration information tothe block 446 uncalibrated LOS 508 to obtain a calibrated LOS 512 inblock 448 and using calibrated LOS 512 in block 120 or block 130 ofmethod 100 (FIG. 1) to determine the user's three-dimensional POG. Theapplication of calibration information in block 448 is an optionalprocedure. By way of non-limiting example, such calibration adjustmentmay be used to account for inaccuracies in: measurement of the physicallocations of the off-axis lights 224A used to generate glints in thedark pupil image; the camera model parameters; and the variations in theeyes of individual users from eye model 500 used in method 440 and theoffset of fovea 510 from optical axis 508 of eye model 500. Beforeapplying calibration information in block 448, such calibrationinformation must be obtained.

FIG. 11 illustrates a method 460 for obtaining calibration informationaccording to a particular embodiment of the invention. Method 460 beginsin block 462 which involves selecting a plurality of known referencepoints p_(i) in the three-dimensional space of the scene presented tothe user. Preferably, the reference points p selected in block 462 arespaced apart over the scene (i.e. the region in which it is desired totrack the user's three-dimensional POG). The number n of referencepoints p_(i) in block 462 may be selected to balance the desire foroptimum calibration with the desire to reduce the calibration timerequired.

Method 460 then performs a calibration loop 464 for each of the nreference points p_(i). In each iteration of calibration loop 464, auser is asked to focus on one of the reference points p_(ti). In block466, the above described methods are used to estimate the uncalibratedLOS 508 for each of the user's eyes when the user is focused on thereference point p_(i). The uncalibrated LOS 508 for the user's eyes whenthe user is focused on the i^(th) reference point p_(i) may bedesignated as LOS _(1,i), LOS _(2,i) where LOS _(1,i) represents theblock 446 uncalibrated Los 508 of the first eye when it is focused onthe i^(th) reference point p_(i) and LOS _(2,i) represents the block 446uncalibrated LOS 508 of the second eye when it is focused on the i^(th)reference point p_(i). The term LOS _(1,i) may be referred to as the“i^(th) calibration LOS value for the first eye” and the term LOS _(2,i)may be referred to as the “i^(th) calibration value for the second eye”.FIG. 14 schematically depicts the i^(th) reference point p_(i) and thei^(th) calibration LOS value for the first eye ( LOS _(1,i)). It isassumed, for simplification, that the corneal center CC₁ of the firsteye (shown as the origin in FIG. 14) coincides with the center ofrotation of the first eye. While the actual center of rotation of thefirst eye is unknown, it is assumed that the center of rotation is atleast relatively close to the corneal center CC₁.

In general, the i^(th) calibration LOS values ( LOS _(1,i), LOS _(2,i))may not intersect exactly with reference point p_(i). This is shown inFIG. 14 for LOS _(1,i). It can be seen from FIG. 14 that LOS _(1,i) maybe characterized, at least in part, by the angles[θ_(los,1,i),φ_(los,1,i)] and that a vector from the corneal center CC₁of the first eye intersecting point p_(i) may be characterized, at leastin part, by the angles [θ_(cal,1,i),φ_(cal,1,i)]. Block 468 (FIG. 11)involves determining a set of angular rotations [θ_(1,i),φ_(1,i)] which,when added to [θ_(los,1,i),φ_(los,1,i)] to rotate LOS _(1,i) aboutcornea center CC₁, will shift LOS _(1,i) to intersect reference pointp_(i) and a set of angular rotations [θ_(2,i), φ_(2,i)] which, whenadded to [θ_(los,1,i),φ_(los,1,i)] to rotate LOS _(2,i) about corneacenter CC₂, will shift LOS _(2,i) to intersect reference point p_(i).That is, block 468 involves determining [θ_(1,i),φ_(1,i)] and[θ_(2,i),φ_(2,i)] which satisfy:

$\begin{matrix}{{\begin{bmatrix}\theta_{{cal},1,i} \\\varphi_{{cal},1,i}\end{bmatrix} = \begin{bmatrix}{\theta_{{los},1,i} + \theta_{1,i}} \\{\varphi_{{los},1,i} + \varphi_{1,i}}\end{bmatrix}}{and}} & (5) \\{\begin{bmatrix}\theta_{{cal},2,i} \\\varphi_{{cal},2,i}\end{bmatrix} = \begin{bmatrix}{\theta_{{los},2,i} + \theta_{2,i}} \\{\varphi_{{los},2,i} + \varphi_{2,i}}\end{bmatrix}} & (6)\end{matrix}$

The angular rotations [θ_(1,i),φ_(1,i)] determined in block 468 may bereferred to as the “i^(th) set of calibration parameters for the firsteye” and the angular rotations [θ_(2,i),φ_(2,i)] determined in block 468may be referred to as the “i^(th) set of calibration parameters for thesecond eye”.

In one particular embodiment, the block 468 determination of the i^(th)set of calibration parameters for the first and second eyes([θ_(1,i),φ_(1,i)],[θ_(2,i),φ_(2,i)]) proceeds as follows. It can easilybe shown from the geometry of FIG. 14, that:

$\begin{matrix}{\left\lbrack \frac{\overset{\_}{{LOS}_{1,i}}}{\overset{\_}{{LOS}_{1,i}}} \right\rbrack = {\begin{bmatrix}{LOS}_{1,i,x} \\{LOS}_{1,i,y} \\{LOS}_{1,i,z}\end{bmatrix} = \begin{bmatrix}{\sin \; \varphi_{{los},1,i}\cos \; \theta_{{los},1,i}} \\{\sin \; \varphi_{{los},1,i}\sin \; \theta_{{los},1,i}} \\{\cos \; \varphi_{{los},1,i}}\end{bmatrix}}} & (7)\end{matrix}$

Based on equation (7), the i^(th) calibration LOS value for the firsteye ( LOS _(1,i)) may be used to solve for [θ_(los,1,i),φ_(los,1,i)].Similarly, i^(th) calibration LOS value for the second eye ( LOS _(2,i))may be used to solve for [θ_(los,2,i),φ_(los,2,i)]. The parameters[θ_(cal,1,i),φ_(cal,1,i)] may be determined using a similar geometriccalculation based on [θ_(cal,2,i),φ_(cal,2,i)] the known coordinates ofthe ith reference point p_(i). Then, equations (5) and (6) may be solvedto resolve the i^(th) set of calibration parameters for the first eye([θ_(1,i),φ_(1,i)]) and the i^(th) set of calibration parameters for thesecond eye ([θ_(2,i),φ_(2,i)]).

Referring back to FIG. 11, after looping through each of the n referencepoints p_(i), method 460 concludes when it has determined n sets ofcalibration parameters for the first eye[θ_(1,i),φ_(1,i)]|_(i=1, 2 . . . n) and n sets of calibration parametersfor the second eye [θ_(2,i),φ_(2,i)]|_(1, 2 . . . n).

FIG. 12 schematically depicts a method 480 for applying calibrationinformation to the block 446 (FIG. 9) uncalibrated LOS vector 508. Themethod 480 application of calibration information to uncalibrated LOSvector 508 may be implemented in block 448 (see FIG. 9). Method 480 ofFIG. 12 may be implemented independently on each of the user's eyes.Method 480 is shown and described in relation to the user's first eye,but it will be appreciated that the application of calibrationinformation to the block 446 uncalibrated LOS vector 508 for the otherone of the user's eyes may be substantially similar. For the purposes ofexplaining method 480, the current block 446 uncalibrated LOS vector 508for the first eye is referred to as LOS _(1,current) and the currentblock 448 calibrated LOS vector 512 for the first eye is referred to asLOS _(1,current,cal). An example showing the current block 446uncalibrated LOS vector 508 for the first eye ( LOS _(1,current)) andthe current block 448 calibrated LOS vector 512 for the first eye ( LOS_(1,current,cal)) is schematically illustrated in FIG. 15.

Method 480 commences in block 482 where a distance (dist_(1,i)) isdetermined between the current uncalibrated LOS vector LOS _(1,current)for the first eye and each of the n block 466 calibration LOS values LOS_(1,i) for the first eye. For the i^(th) calibration LOS value LOS_(1,i), the distance dist_(1,i) may be determined according to:

dist_(1,i)=∥ LOS _(1,current)− LOS _(1,i)∥  (8)

where ∥∥ represents the norm operator. The output of block 482 includesn distinct dist_(1,i) values corresponding to each of the n calibrationLOS values LOS _(1,i) for the first eye. In other embodiments, othermetrics (i.e. other than the geometrical norm) may be used to determinethe dist_(1,i) values.

Method 480 then proceeds to block 484 which involves an inquiry intowhether any of the dist_(1,i) values are zero. If the block 484 inquiryis negative (i.e. none of the dist_(1,i) values are zero—block 484 NOoutput), then method 480 proceeds to block 488. Block 488 involvesdetermining n weighting factors w_(1,i) (i.e. one weighting factorcorresponding to each of the n sets of calibration parameters[θ_(1,i),φ_(1,i)]). According to one particular embodiment, the i^(th)weighting factor w_(1,i) for the first eye may be calculated in block488 according to:

$\begin{matrix}{w_{1,i} = \frac{1}{{dist}_{1,i} \cdot {\sum\limits_{i = 1}^{n}\frac{1}{{dist}_{1,i}}}}} & (9)\end{matrix}$

Where w_(1,i) is a weighting factor proportional to the inverse ofdist_(1,i) i.e. the smaller dist_(1,i).becomes, the closer w_(1,i) getsto unity.

Method 480 then proceeds to block 490 which involves calculating theweighted calibration parameters [θ₁,φ₁] to be applied to the currentuncalibrated LOS vector LOS _(1,current) for the first eye. In oneparticular embodiment, the weighted calibration parameters [θ₁,φ₁] maybe calculated in block 490 according to:

$\begin{matrix}{{\theta_{1} = {\sum\limits_{i = 1}^{n}{w_{1,i}\theta_{1,i}}}}{and}} & (10) \\{\varphi_{1} = {\sum\limits_{i = 1}^{n}{w_{1,i}\varphi_{1,i}}}} & (11)\end{matrix}$

Method 480 then proceeds to block 492, where the weighted calibrationparameters [θ₁,φ₁] are applied to the current uncalibrated LOS vectorLOS _(1,current) to obtain the calibrated LOS vector LOS_(1,current,cal). The block 492 application of the weighted calibrationparameters [θ₁,φ₁] to LOS _(1,current) may involve rotating LOS_(1,current) by the angles [θ₁,φ₁] about the corneal center CC₁ of model500 for the first eye to obtain LOS _(1,current,cal). A particularembodiment of the block 492 application of the weighted calibrationparameters [θ₁,φ₁] to LOS _(1,current) is schematically illustrated inFIG. 15. It can easily be shown from the geometry of FIG. 15, that:

$\begin{matrix}{\left\lbrack \frac{\overset{\_}{{LOS}_{1,{current}}}}{\overset{\_}{{LOS}_{1,{current}}}} \right\rbrack = {\begin{bmatrix}{LOS}_{1,{current},x} \\{LOS}_{1,{current},y} \\{LOS}_{1,{current},z}\end{bmatrix} = \begin{bmatrix}{\sin \; \varphi_{1,{current}}\cos \; \theta_{1,{current}}} \\{\sin \; \varphi_{1,{current}}\sin \; \theta_{1,{current}}} \\{\cos \; \varphi_{1,{current}}}\end{bmatrix}}} & (12)\end{matrix}$

Based on equation (12), the current uncalibrated LOS vector LOS_(1,current) may be used to solve for [θ_(1,current),φ_(1,current)]. Theweighted calibration parameters [θ₁,φ₁] may then be added to angles[θ_(1,current),φ_(1,current)] associated with the current uncalibratedLOS vector LOS _(1,current) to obtain corresponding angles[θ_(1,current,cal),φ_(1,current,cal)] associated with the calibrated LOSvector LOS _(1,current,cal) in accordance with:

$\begin{matrix}{\begin{bmatrix}\theta_{1,{current},{cal}} \\\varphi_{1,{current},{cal}}\end{bmatrix} = \begin{bmatrix}{\theta_{1,{current}} + \theta_{1}} \\{\varphi_{1,{current}} + \varphi_{1}}\end{bmatrix}} & (13)\end{matrix}$

and the calibrated LOS vector LOS _(1,current,cal) may then becalculated using [θ_(1,current,cal),φ_(1,current,cal)] in accordancewith:

$\begin{matrix}{\left\lbrack \frac{\overset{\_}{{LOS}_{1,{current},{cal}}}}{\overset{\_}{{LOS}_{1,{current},{cal}}}} \right\rbrack = {\quad{\begin{bmatrix}{LOS}_{1,{current},{cal},x} \\{LOS}_{1,{current},{cal},y} \\{LOS}_{1,{current},{calz}}\end{bmatrix} = \begin{bmatrix}{\sin \; \varphi_{1,{current},{cal}}\cos \; \theta_{1,{current},{cal}}} \\{\sin \; \varphi_{1,{current},{cal}}\sin \; \theta_{1,{current},{cal}}} \\{\cos \; \varphi_{1,{current},{cal}}}\end{bmatrix}}}} & (14)\end{matrix}$

In summary, the method 480 calibration process adds a set of weightedcalibration parameters [θ₁,φ₁] to the angles [θ_(los,1,i),φ_(los,1,i)]associated with the current uncalibrated LOS vector LOS _(1,current) toobtain the angles [θ_(1,current,cal),φ_(1,current,cal)] associated withthe calibrated LOS vector LOS _(1,current,cal). The weighted calibrationparameters [θ₁,φ₁] are a linear combination of n individual sets ofcalibration parameters [θ_(1,i),φ_(1,i)]|_(i-1 . . . n) and the weightw_(1,i) applied to the i^(th) individual set of calibration parameters[θ_(1,i),φ_(1,i)] depends on the proximity of the current uncalibratedLOS vector LOS _(1,current) to the i^(th) calibration LOS value LOS_(1,i).

If one of the dist_(1,i) values is zero (block 484 YES output), then thecurrent uncalibrated LOS vector LOS _(1,current) is the same as thecorresponding one of the n calibration LOS values LOS _(1,i) and method480 proceeds to block 486. Block 486 may involve adding the unweightedcalibration parameters [θ_(1,i),φ_(1,i)] corresponding to the value of ifor which dist_(1,i)=0 directly to the angles [θ_(los,1,i),φ_(los,1,i)]associated with the current uncalibrated LOS vector LOS _(1,current) toobtain the angles [θ_(1,current,cal),φ_(1,current,cal)] associated withthe calibrated LOS vector LOS _(1,current,cal). This may be accomplishedusing a process similar to the process of block 492 described above,except that the weighted calibration parameters [θ₁,φ₁] are replacedwith the unweighted calibration parameters [θ_(1,i),φ_(1,i)]corresponding to the value of i for which dist_(1,i)=0.

Another technique that may be used in particular embodiments to improvethe method 100 POG estimation involves the use of one or more finiteimpulse response (FIR) moving average filters which may remove some ofthe high frequency jitter experienced in the method 100 POG estimationtechnique. The filtering procedures described below may be performed inaddition to the calibration procedures described above. In someembodiments, FIR filtering may be performed by a suitably programmeddigital signal processing (DSP) unit which may be a part of, orotherwise controlled by, controller 220. The general operation of FIRmoving average filters are well known to those skilled in the art.

In some embodiments, FIR moving average filters may be applied tovarious elements of method 100 (FIG. 1). In one particular embodiment,the three-dimensional POG determined in block 140 may be filtered usinga FIR moving average filter. In addition or in the alternative, otherparameters which may be filtered include, without limitation: thedirections of the calibrated or uncalibrated LOS vectors (block 446,block 448, block 120, and/or block 130); fine pupil information (block320); the locations of the multiple glints (block 322); the locations ofcornea centers CC₁, CC₂ (block 442); and/or the locations of model pupilcenters P_(c1), P_(c2) (block 444). It will be appreciated that some ofthese filters may be applied independently to the parameter(s) of bothof the user's eyes. These moving average filters may be applied on eachiteration of method 100 (FIG. 1), method 300 (FIG. 4), method 350 (FIG.6), method 370 (FIG. 7), method 400 (FIG. 8), method 600 (FIGS. 17A,17B), method 440 (FIG. 9), method 480 (FIG. 12) and/or any of the othermethods described herein.

In some embodiments, the filtering process includes a method forfixation detection which detects when one or more parameters associatedwith the user's POG has shifted significantly and, in response to such ashift, clears the filter history to avoid spurious results which mayotherwise be caused by the moving average filter. By way of non-limitingexample, parameters for which detection of a significant shift may causea filter clearing event include: the three-dimensional POG estimateitself, the directions of the calibrated or uncalibrated LOS vectors,fine pupil information in the image data; the locations of the multipleglints in the image data; the locations of cornea centers CC₁, CC₂;and/or the locations of model pupil centers P_(c1), P_(c2). FIG. 13depicts a method 500 for fixation detection and filter updatingaccording to a particular embodiment of the invention. Method 500 may beapplied to any one or more of the filtered parameter(s) of method 100,including any of the example parameters listed above. For the purposesof explanation, it is assumed that the method 500 parameter is thethree-dimensional POG determined in block 140 (FIG. 1).

Method 500 commences in block 501 where a first raw parameter (e.g. POG)value is obtained. Since there is no historical POG data, the raw POGvalue obtained in block 501 is used as the filtered POG value for thepurposes of the final POG estimate. Method 500 then proceeds to block502 which involves getting a new raw parameter (e.g. POG) value. Inblock 502, there is/are historical parameter (e.g. POG) value(s).Consequently, the raw POG value is retained, but the system applies themoving average filter to the raw POG value and uses the filtered POGvalue as its estimate of the user's current POG. Method 500 thenproceeds to block 504. Block 504 involves an inquiry into whether thereare a sufficient number (n) of raw parameter (e.g. POG) values within afirst threshold region (i.e. sufficiently close to one another) toconclude that the user eyes are fixated on something. The firstthreshold region may be different depending on the nature of theparameter being filtered. For example, where the parameter beingfiltered in the POG estimate or a direction of a calibrated oruncalibrated LOS vector in the principal coordinate system, then thefirst threshold region may represent a region of space in the principalcoordinate system. As another example, where the parameter beingfiltered is the center of the pupil in the fine pupil image data, thenthe first threshold region may represent a region of pixels in the imagedata.

The number n of raw POG values may vary depending on the application towhich system 210 is being put to use and on the sampling rate (e.g. therate of iteration of method 100). For example, when system 210 is beingused as a pointing device for a man/machine user interface, the ratio ofthe number n to the sampling rate may be relatively low (e.g. in a rangeof 0.01-0.10 seconds), such that method 500 quickly determines newfixation locations, thereby allowing the user to interact relativelyquickly with the user interface. By way of contrasting example, whensystem 210 is being used to evaluate the functionality of the user'seyes, the ratio of the number n to the sampling rate may be setrelatively high (e.g. in a range of 0.25-4 seconds), such that afixation is only determined by method 500 after the user has beenstaring at the same location for a period of time.

The dimensions of the first threshold region may also depend on theapplication to which system 210 is being put to use. For example, wherethe parameter being filtered in method 500 is the POG estimate andsystem 210 is being used to select between closely spaced POGs, then thedimension(s) of the first threshold region may be relatively small (e.g.less than 2 cm³), such that system 210 is able to discern betweendifferent POGs without mistakenly concluding that the user is fixated ona particular POG. On the other hand, when the POG locations for whichsystem 210 is required to discern are relatively spaced apart, then thefirst threshold region may be relatively large (e.g. greater than 2cm³), such that a fixation may be established relatively quickly bymethod 500. The center of the first threshold region may be the averageof the n raw parameter (e.g. POG) values or, in some embodiments, theaverage of some other number of recently obtained raw parameter (e.g.POG) values.

If the block 504 inquiry determines that there is no fixation (block 504NO output), then method 500 returns to block 502 to obtain another rawparameter value. If it is concluded in block 504 that a user's eyes arefixated (i.e. there are n raw parameter values within the firstthreshold region block 504 YES output), then method 500 proceeds toblock 506, where a fixation is established. Block 506 may involvetoggling a boolean variable, for example. After block 506, method 500proceeds to block 508 which involves obtaining another raw parametervalue. Method then proceeds to block 510. Block 510 involves an inquiryinto whether the block 508 raw parameter value is within a secondthreshold region.

If the block 510 inquiry determines that the block 508 raw parametervalue is within the second threshold region (block 510 YES output), thenmethod 500 concludes that the user is still focusing on the same regionof space (i.e. the user's eyes are fixated) and the filter history ismaintained, but if the block 510 inquiry determines that the block 508raw parameter value is outside of the second threshold region (block 510NO output), then method 500 concludes that the user is changing his orher fixation and the filter history is cleared. In general, the secondthreshold region used in the block 510 inquiry may be different than thefirst threshold region used in the block 504 inquiry, although this isnot necessary. The center of the second threshold region may be theaverage of some suitable number of recently obtained raw parametervalues. The boundary of the second threshold region may be selected onthe basis of criteria similar to the above-discussed criteria used toselect the boundary of the first threshold region.

If the block 508 raw parameter value is within the second thresholdregion (block 510 YES output), then method 500 proceeds to block 512which involves filtering the block 508 raw parameter value and using thefiltered parameter value in the estimate of the user's current POG. If,on the other hand, the block 508 raw parameter value is outside of thesecond threshold region (block 510 NO output), then method 500 proceedsto block 514. Block 514 involves clearing the filter history to avoidobtaining spurious results when method 500 concludes that the user isshifting their POG. After block 514, method 500 loops back to block 501and method 500 is repeated again.

As discussed above, method 500 may be applied to a variety of parameters(e.g. other than POG) for which filtering may be desirable. In caseswhere method 500 is applied to other parameter(s), it may be desirableto select the characteristics on the first and second threshold regionson the basis of other criteria.

Certain implementations of the invention comprise computer processorswhich execute software instructions which cause the processors toperform a method of the invention. For example, one or more processorsin a point of gaze estimation system may implement data processing stepsin the methods described herein by executing software instructionsretrieved from a program memory accessible to the processors.

The invention may also be provided in the form of a program product. Theprogram product may comprise any medium which carries a set ofcomputer-readable instructions which, when executed by a data processor,cause the data processor to execute a method of the invention. Programproducts according to the invention may be in any of a wide variety offorms. The program product may comprise, for example, physical mediasuch as magnetic data storage media including floppy diskettes, harddisk drives, optical data storage media including CD ROMs, DVDs,electronic data storage media including ROMs, flash RAM, or the like.The instructions may be present on the program product in encryptedand/or compressed formats.

Where a component (e.g. a software module, processor, assembly, device,circuit, etc.) is referred to above, unless otherwise indicated,reference to that component (including a reference to a “means”) shouldbe interpreted as including as equivalents of that component anycomponent which performs the function of the described component (i.e.that is functionally equivalent), including components which are notstructurally equivalent to the disclosed structure which performs thefunction in the illustrated exemplary embodiments of the invention.

As will be apparent to those skilled in the art in the light of theforegoing disclosure, many alterations and modifications are possible inthe practice of this invention without departing from the spirit orscope thereof. For example:

-   -   Blocks 142, 144 described above represent one technique for        using the first and second LOS vectors ( LOS₁ , LOS₂ ) to        determine the three-dimensional POG in block 140. In other        embodiments, block 140 may be implemented using other        techniques. For example, referring to FIG. 2, one can use the        cross-product operator x to define a normal vector n= LOS₁ ×        LOS₂ which is perpendicular to both the first and second LOS        vectors ( LOS₁ , LOS₂ ). The magnitude of the closest distance d        between the first and second LOS vectors ( LOS₁ , LOS₂ ) can        then be determined by projecting the vector r=[CC₁−CC₂] onto the        unit normal vector

$\hat{n} = \frac{\overset{\_}{n}}{\overset{\_}{n}}$

-   -    in accordance with d=| r·{circumflex over (n)}|. Referring to        the vector W=[P(s)−Q(t)] shown in FIG. 2, we can then express a        system of three equations:

LOS₁ · W=0

LOS₂ · W=0

W· W=d ²

-   -    which may be solved for the three variables w_(x), w_(y), w_(z)        where W=[w_(y), w_(y), w_(z)]. It should be noted that because        of the d² term, there will be two solutions to the above system        of equations. Recognizing, from the above discussion, that        P(s)=[ CC₁ +s LOS₁ ] and Q(t)=[ CC₂ +t LOS₂ ], one may divide        the equation W=[P(s)−Q(t)] into its component parts (i.e.        w_(x)=CC_(1,x)+sLOS_(1,x)−CC_(2,x)−tLOS_(2,x),        w_(y)=CC_(1,y)+sLOS_(1,y)−CC_(2,y)−tLOS_(2,y),        w_(z)=CC_(1,z)+sLOS_(1,z)−CC_(2,z)−tLOS_(2,z)) to yield three        equations in two unknowns (s, t). This system may be solved to        yield s and t, which in turn may be used to calculate P(s), Q(t)        and W. The three-dimensional POG can then be determined to be        the midpoint of W (between the points P(s) and Q(t)).    -   The FIR moving average filters described above may be replaced        with other suitable types of filters which may serve to reduce        high frequency jitter in the method 100 POG estimation.    -   Some of the methods described above (e.g. method 400 (FIG. 4),        method 350 (FIG. 6), method 370 (FIG. 6), method 400 (FIG. 8),        method 600 (FIGS. 17A, 17B), method 440 (FIG. 9) and method 480        (FIG. 12)) are shown and described in relation to one of the        user's eyes, but it will be appreciated that these methods may        be applied in a similar manner to the other one of the user's        eyes. Others of the methods described above (e.g. method 100        (FIG. 1), method 460 (FIG. 11) and method 500 (FIG. 13)) may        involve both of the user's eyes.    -   In some applications, various aspects of the geometry of objects        within the scene presented to the user may be known. By way of        non-limiting example, where the scene presented to the user        includes a computer screen, the screen may be described in three        dimensions by a planar surface in the principal coordinate        system provided that three points on the screen are known and        that the boundary of the screen is known. As another        non-limiting example, a round ball may be represented by a        spherical surface with a known center and a known radius in the        principal coordinate system. Objects of general shape may be        described using a fine mesh of polygons, whose polygonal        characteristics are known in the principal coordinate system. In        cases where the geometry of the scene or objects in the scene is        known, then it is possible to estimate a three-dimensional POG        by determining the intersection of a single LOS vector ( LOS₁ ,        LOS₂ ) with the known geometry of an object in the scene. Once        an LOS vector ( LOS₁ , LOS₂ ) is determined in the principal        coordinate system, then geometrical calculations may be        performed to determine the intersection point(s) of one of the        LOS vectors with objects of known geometry. Such geometrical        calculations are well known in the art and depend on the        geometry of the object in the scene. In some applications,        accuracy and/or precision may be improved by determining LOS        vectors ( LOS₁ , LOS₂ ) for both eyes, determining the        intersection point(s) of both LOS vectors ( LOS₁ , LOS₂ ) with        the known geometry of the object in the scene and averaging        corresponding intersection points from both eyes to arrive at        the three-dimensional POG estimate. As discussed above, the same        scene is preferably presented to both of the user's eyes. The        scene presented to the user may be the real world.    -   Certain features of the systems and methods described herein are        applicable to detecting a single LOS vector and using the single        LOS vector in combination with known objects in the scene (e.g.        a planar monitor screen or another object with a known geometry)        to predict the user's POG in the scene. For example, a POG may        be determined to be the point where a single LOS vector        intersects the known location of the object. Particular features        which may be used in connection with a single LOS include,        without limitation: methods for obtaining fine pupil particulars        in the image data (FIGS. 4, 6, 7 and 8), methods for obtaining        glint particulars in the image data (FIGS. 4 and 7), calibration        methods (FIGS. 9, 10, 11, 12, 14 and 15), filtering methods        (FIG. 13) and methods for mapping glints detected in the image        data to particular off-axis lights (FIGS. 16, 17 and 18).        Accordingly, the invention should be construed in accordance        with the following claims.

What is claimed is:
 1. A method for determining a point-of-gaze (POG) ofa user in three dimensions, the method comprising: presenting athree-dimensional scene to the user; capturing image data which includesimages of both eyes of the user using a single image capturing device,the image capturing device capturing image data from a single point ofview having a single corresponding optical axis; estimating a firstline-of-sight (LOS) vector in a three-dimensional coordinate system fora first of the user's eyes based on the image data captured by thesingle image capturing device; estimating a second LOS vector in thethree-dimensional coordinate system for a second of the user's eyesbased on the image data captured by the single image capturing device;determining the three-dimensional POG of the user in the scene in thethree-dimensional coordinate system using the first and second LOSvectors as estimated based on the image data captured by the singleimage capturing device.
 2. A method according to claim 1 wherein thescene is a region of the real world and the three-dimensional coordinatesystem is a system for identifying a location of one or more points inthe real world.
 3. A method according to claim 1 wherein the scenecomprises a region of the real world in which a virtual scene isdisplayed and the three-dimensional coordinate system is a system foridentifying a location of one or more points in the virtual scene basedon a corresponding location of the one or more points in the real world.4. A method according to claim 1 wherein estimating the first and secondLOS vectors comprises, for each eye, using a plurality of image featureswithin the image data to estimate a center of the cornea of the eye inthe three-dimensional coordinate system.
 5. A method according to claim4 wherein, for each eye, the plurality of image features comprises acorresponding plurality of glints and each glint comprises a reflectionof a corresponding light source from the eye.
 6. A method according toclaim 5 wherein each light source is positioned at a correspondingoff-axis location that is spaced apart from an optical axis of an imagecapturing device which captures the image data.
 7. A method according toclaim 1 wherein estimating the first and second LOS vectors comprises,for each eye: detecting a plurality of glints within the image data,each glint comprising a reflection from the eye; establishing acorrespondence between each of the glints and a corresponding lightsource; using at least two of the plurality of glints and thecorrespondence between the at least two glints and their correspondinglight sources to estimate a center of the cornea of the eye in thethree-dimensional coordinate system.
 8. A method according to claim 7wherein each light source is positioned at a corresponding off-axislocation that is spaced apart from an optical axis of an image capturingdevice which captures the image data.
 9. A method according to claim 8wherein the off-axis locations of the light sources arenon-symmetrically distributed about the optical axis.
 10. A methodaccording to claim 7 wherein, for each eye, establishing thecorrespondence between each of the glints and the corresponding lightsource comprises performing a pattern matching process between thedetected plurality of glints and a plurality of reference points whereina correspondence between the reference points and the light sources isknown.
 11. A method according to claim 7 wherein using the glints andthe correspondence between the glints and their corresponding lightsources to estimate a center of the cornea of the eye in thethree-dimensional coordinate system comprises selecting a subsetplurality of glints from among the plurality of glints and using thesubset plurality of glints and the correspondence between the subsetplurality of glints and its corresponding light sources to estimate thecenter of the cornea of the eye in the three-dimensional coordinatesystem and wherein selecting the subset plurality of glints from amongthe plurality of glints comprises: analyzing the image data to estimatea center of a pupil image of the eye within the image data and choosingthe subset plurality of glints closest to the center of the pupil image.12. A method according to claim 11 wherein the subset plurality ofglints comprises two glints.
 13. A method according to claim 11 wherein,for each eye, establishing the correspondence between each of the glintsand the corresponding light source comprises performing a patternmatching process between the detected plurality of glints and aplurality of reference points wherein a correspondence between thereference points and the light sources is known.
 14. A method accordingto claim 4 comprising, for each eye, analyzing the image data toestimate characteristics of a pupil image of the eye within the imagedata.
 15. A method according to claim 14 wherein, for each eye,analyzing the image data to estimate characteristics of the pupil imagewithin the image data comprises estimating a center of the pupil imagein the image data and wherein estimating the first and second LOSvectors comprises, for each eye, tracing a ray from the center of thepupil image in the image data into a model of the eye in thethree-dimensional coordinate system to estimate a location of a centerof the pupil in the three-dimensional coordinate system.
 16. A methodaccording to claim 15 wherein estimating the first and second LOSvectors comprises, for each eye, determining the LOS vector to be on aline from the center of the cornea of the eye through the center of thepupil of the eye in the three-dimensional coordinate system.
 17. Amethod according to claim 4 wherein, for each eye, using the glints andthe correspondence between the glints and their corresponding lightsources to estimate a center of the cornea of the eye in thethree-dimensional coordinate system comprises using a model of the eyewhere one or more parameters of the model of the eye are measured fromthe user's eye.
 18. A method according to claim 1 wherein capturing theimage data comprises: capturing a dark pupil image illuminated by aplurality of off-axis light sources, the off-axis light sources beingpositioned at locations away from an optical axis of an image-capturingdevice that captures the image data; and capturing a bright pupil imageilluminated by one or more on-axis light sources, the one or moreon-axis light sources being positioned at one or more correspondinglocations relatively close to the optical axis when compared to thelocations of the off-axis light sources.
 19. A method according to claim1 comprising, for each eye, adjusting the LOS vector in thethree-dimensional coordinate system using one or more weightedcalibration values to obtain a calibrated LOS vector.
 20. A methodaccording to claim 19 wherein determining the three-dimensional POG ofthe user in the scene in the three-dimensional coordinate systemcomprises, for each of the first and second LOS vectors, using thecalibrated LOS vector in place of the LOS vector.
 21. A method accordingto claim 1 comprising applying a moving average filter to successiveestimations of at least one of: the POG in the three-dimensionalcoordinate system; the first LOS vector in the three-dimensionalcoordinate system; and the second LOS vector in the three-dimensionalcoordinate system; to obtain a filtered three-dimensional POG.
 22. Amethod according to claim 21 comprising clearing historical values fromthe moving average filter upon determining that the user has changedfrom a first fixation location to a second fixation location.
 23. Amethod according to claim 15 wherein estimating the first and second LOSvectors comprises, for each eye, determining the LOS vector to be on aline from the center of the cornea of the eye through the center of thepupil of the eye in the three-dimensional coordinate system.